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100 | Knowledge Mining With Scene Text for Fine-Grained Recognition | [
"Hao Wang",
"Junchao Liao",
"Tianheng Cheng",
"Zewen Gao",
"Hao Liu",
"Bo Ren",
"Xiang Bai",
"Wenyu Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Knowledge_Mining_With_Scene_Text_for_Fine-Grained_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Knowledge_Mining_With_Scene_Text_for_Fine-Grained_Recognition_CVPR_2022_paper.pdf | null | 2203.14215 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Hao and Liao, Junchao and Cheng, Tianheng and Gao, Zewen and Liu, Hao and Ren, Bo and Bai, Xiang and Liu, Wenyu},
title = {Knowledge Mining With Scene Text for Fine-Grained Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | Recently, the semantics of scene text has been proven to be essential in fine-grained image classification. However, the existing methods mainly exploit the literal meaning of scene text for fine-grained recognition, which might be irrelevant when it is not significantly related to objects/scenes. We propose an end-to-... | [
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101 | Self-Supervised Learning of Object Parts for Semantic Segmentation | [
"Adrian Ziegler",
"Yuki M. Asano"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Ziegler_Self-Supervised_Learning_of_Object_Parts_for_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ziegler_Self-Supervised_Learning_of_CVPR_2022_supplemental.pdf | 2204.13101 | cvf | @InProceedings{Ziegler_2022_CVPR,
author = {Ziegler, Adrian and Asano, Yuki M.},
title = {Self-Supervised Learning of Object Parts for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Progress in self-supervised learning has brought strong general image representation learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks such as unsupervised image segmentation have not benefited from this trend as they require spatially-diverse representations. However, learning... | [
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102 | Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects | [
"Manuel Stoiber",
"Martin Sundermeyer",
"Rudolph Triebel"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Stoiber_Iterative_Corresponding_Geometry_Fusing_Region_and_Depth_for_Highly_Efficient_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Stoiber_Iterative_Corresponding_Geometry_Fusing_Region_and_Depth_for_Highly_Efficient_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Stoiber_Iterative_Corresponding_Geometry_CVPR_2022_supplemental.pdf | 2203.05334 | cvf | @InProceedings{Stoiber_2022_CVPR,
author = {Stoiber, Manuel and Sundermeyer, Martin and Triebel, Rudolph},
title = {Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision. State-of-the-art approaches often rely on object texture to tackle this problem. However, while they achieve impressive results, many objects do not contain sufficient texture, violating the main underlying assumption. ... | [
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103 | Single-Photon Structured Light | [
"Varun Sundar",
"Sizhuo Ma",
"Aswin C. Sankaranarayanan",
"Mohit Gupta"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Sundar_Single-Photon_Structured_Light_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Sundar_Single-Photon_Structured_Light_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sundar_Single-Photon_Structured_Light_CVPR_2022_supplemental.pdf | 2204.05300 | cvf | @InProceedings{Sundar_2022_CVPR,
author = {Sundar, Varun and Ma, Sizhuo and Sankaranarayanan, Aswin C. and Gupta, Mohit},
title = {Single-Photon Structured Light},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We present a novel structured light technique that uses Single Photon Avalanche Diode (SPAD) arrays to enable 3D scanning at high-frame rates and low-light levels. This technique, called "Single-Photon Structured Light", works by sensing binary images that indicates the presence or absence of photon arrivals during eac... | [
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104 | Deblurring via Stochastic Refinement | [
"Jay Whang",
"Mauricio Delbracio",
"Hossein Talebi",
"Chitwan Saharia",
"Alexandros G. Dimakis",
"Peyman Milanfar"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Whang_Deblurring_via_Stochastic_Refinement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Whang_Deblurring_via_Stochastic_CVPR_2022_supplemental.pdf | 2112.02475 | cvf | @InProceedings{Whang_2022_CVPR,
author = {Whang, Jay and Delbracio, Mauricio and Talebi, Hossein and Saharia, Chitwan and Dimakis, Alexandros G. and Milanfar, Peyman},
title = {Deblurring via Stochastic Refinement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead... | [
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105 | 3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds | [
"Daigang Cai",
"Lichen Zhao",
"Jing Zhang",
"Lu Sheng",
"Dong Xu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_3DJCG_A_Unified_Framework_for_Joint_Dense_Captioning_and_Visual_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Cai_2022_CVPR,
author = {Cai, Daigang and Zhao, Lichen and Zhang, Jing and Sheng, Lu and Xu, Dong},
title = {3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Observing that the 3D captioning task and the 3D grounding task contain both shared and complementary information in nature, in this work, we propose a unified framework to jointly solve these two distinct but closely related tasks in a synergistic fashion, which consists of both shared task-agnostic modules and lightw... | [
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106 | TransGeo: Transformer Is All You Need for Cross-View Image Geo-Localization | [
"Sijie Zhu",
"Mubarak Shah",
"Chen Chen"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_TransGeo_Transformer_Is_All_You_Need_for_Cross-View_Image_Geo-Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_TransGeo_Transformer_Is_All_You_Need_for_Cross-View_Image_Geo-Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_TransGeo_Transformer_Is_CVPR_2022_supplemental.pdf | 2204.00097 | cvf | @InProceedings{Zhu_2022_CVPR,
author = {Zhu, Sijie and Shah, Mubarak and Chen, Chen},
title = {TransGeo: Transformer Is All You Need for Cross-View Image Geo-Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | The dominant CNN-based methods for cross-view image geo-localization rely on polar transform and fail to model global correlation. We propose a pure transformer-based approach (TransGeo) to address these limitations from a different perspective. TransGeo takes full advantage of the strengths of transformer related to g... | [
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107 | R(Det)2: Randomized Decision Routing for Object Detection | [
"Yali Li",
"Shengjin Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RDet2_Randomized_Decision_Routing_for_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_RDet2_Randomized_Decision_Routing_for_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_RDet2_Randomized_Decision_CVPR_2022_supplemental.pdf | 2204.00794 | title_snapshot | @InProceedings{Li_2022_CVPR,
author = {Li, Yali and Wang, Shengjin},
title = {R(Det)2: Randomized Decision Routing for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages ... | In the paradigm of object detection, the decision head is an important part, which affects detection performance significantly. Yet how to design a high-performance decision head remains to be an open issue. In this paper, we propose a novel approach to combine decision trees and deep neural networks in an end-to-end l... | [
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108 | Abandoning the Bayer-Filter To See in the Dark | [
"Xingbo Dong",
"Wanyan Xu",
"Zhihui Miao",
"Lan Ma",
"Chao Zhang",
"Jiewen Yang",
"Zhe Jin",
"Andrew Beng Jin Teoh",
"Jiajun Shen"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Abandoning_the_Bayer-Filter_To_See_in_the_Dark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Abandoning_the_Bayer-Filter_CVPR_2022_supplemental.pdf | 2203.04042 | cvf | @InProceedings{Dong_2022_CVPR,
author = {Dong, Xingbo and Xu, Wanyan and Miao, Zhihui and Ma, Lan and Zhang, Chao and Yang, Jiewen and Jin, Zhe and Teoh, Andrew Beng Jin and Shen, Jiajun},
title = {Abandoning the Bayer-Filter To See in the Dark},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Low-light image enhancement, a pervasive but challenging problem, plays a central role in enhancing the visibility of an image captured in a poor illumination environment. Due to the fact that not all photons can pass the Bayer-Filter on the sensor of the color camera, in this work, we first present a De-Bayer-Filter s... | [
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109 | SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention | [
"Matthias Wödlinger",
"Jan Kotera",
"Jan Xu",
"Robert Sablatnig"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wodlinger_SASIC_Stereo_Image_Compression_With_Latent_Shifts_and_Stereo_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wodlinger_SASIC_Stereo_Image_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Wodlinger_2022_CVPR,
author = {W\"odlinger, Matthias and Kotera, Jan and Xu, Jan and Sablatnig, Robert},
title = {SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this informat... | [
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110 | Exploiting Temporal Relations on Radar Perception for Autonomous Driving | [
"Peizhao Li",
"Pu Wang",
"Karl Berntorp",
"Hongfu Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Exploiting_Temporal_Relations_on_Radar_Perception_for_Autonomous_Driving_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Exploiting_Temporal_Relations_CVPR_2022_supplemental.pdf | 2204.01184 | cvf | @InProceedings{Li_2022_CVPR,
author = {Li, Peizhao and Wang, Pu and Berntorp, Karl and Liu, Hongfu},
title = {Exploiting Temporal Relations on Radar Perception for Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all-weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surroundi... | [
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111 | Multi-Instance Point Cloud Registration by Efficient Correspondence Clustering | [
"Weixuan Tang",
"Danping Zou"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Multi-Instance_Point_Cloud_Registration_by_Efficient_Correspondence_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Multi-Instance_Point_Cloud_Registration_by_Efficient_Correspondence_Clustering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Multi-Instance_Point_Cloud_CVPR_2022_supplemental.zip | 2111.14582 | cvf | @InProceedings{Tang_2022_CVPR,
author = {Tang, Weixuan and Zou, Danping},
title = {Multi-Instance Point Cloud Registration by Efficient Correspondence Clustering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud. Existing solutions require sampling a lot of hypotheses to detect possible instances and reject the outliers, whose robustness and efficiency degrade notably when the number of instances and outli... | [
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112 | Contrastive Boundary Learning for Point Cloud Segmentation | [
"Liyao Tang",
"Yibing Zhan",
"Zhe Chen",
"Baosheng Yu",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Contrastive_Boundary_Learning_CVPR_2022_supplemental.pdf | 2203.05272 | cvf | @InProceedings{Tang_2022_CVPR,
author = {Tang, Liyao and Zhan, Yibing and Chen, Zhe and Yu, Baosheng and Tao, Dacheng},
title = {Contrastive Boundary Learning for Point Cloud Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which degenerates the overall segmentation performance. In this paper, we focus on the segmentation of scene boundaries. Accordingly, we first explor... | [
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113 | Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution | [
"Jie Liang",
"Hui Zeng",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Details_or_Artifacts_A_Locally_Discriminative_Learning_Approach_to_Realistic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Details_or_Artifacts_A_Locally_Discriminative_Learning_Approach_to_Realistic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_Details_or_Artifacts_CVPR_2022_supplemental.pdf | 2203.09195 | cvf | @InProceedings{Liang_2022_CVPR,
author = {Liang, Jie and Zeng, Hui and Zhang, Lei},
title = {Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Single image super-resolution (SISR) with generative adversarial networks (GAN) has recently attracted increasing attention due to its potentials to generate rich details. However, the training of GAN is unstable, and it often introduces many perceptually unpleasant artifacts along with the generated details. In this p... | [
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114 | CVNet: Contour Vibration Network for Building Extraction | [
"Ziqiang Xu",
"Chunyan Xu",
"Zhen Cui",
"Xiangwei Zheng",
"Jian Yang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_CVNet_Contour_Vibration_Network_for_Building_Extraction_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Xu_2022_CVPR,
author = {Xu, Ziqiang and Xu, Chunyan and Cui, Zhen and Zheng, Xiangwei and Yang, Jian},
title = {CVNet: Contour Vibration Network for Building Extraction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently. Inspired by the physical vibration theory, we propose a contour vibration network (CVNet) for automatic building boundary delineation. Different from the previous contour mo... | [
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115 | Hyperbolic Image Segmentation | [
"Mina Ghadimi Atigh",
"Julian Schoep",
"Erman Acar",
"Nanne van Noord",
"Pascal Mettes"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Atigh_Hyperbolic_Image_Segmentation_CVPR_2022_supplemental.pdf | 2203.05898 | title_snapshot | @InProceedings{Atigh_2022_CVPR,
author = {Atigh, Mina Ghadimi and Schoep, Julian and Acar, Erman and van Noord, Nanne and Mettes, Pascal},
title = {Hyperbolic Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pix... | [
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116 | Forward Compatible Training for Large-Scale Embedding Retrieval Systems | [
"Vivek Ramanujan",
"Pavan Kumar Anasosalu Vasu",
"Ali Farhadi",
"Oncel Tuzel",
"Hadi Pouransari"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Ramanujan_Forward_Compatible_Training_for_Large-Scale_Embedding_Retrieval_Systems_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Ramanujan_Forward_Compatible_Training_for_Large-Scale_Embedding_Retrieval_Systems_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ramanujan_Forward_Compatible_Training_CVPR_2022_supplemental.pdf | 2112.02805 | cvf | @InProceedings{Ramanujan_2022_CVPR,
author = {Ramanujan, Vivek and Vasu, Pavan Kumar Anasosalu and Farhadi, Ali and Tuzel, Oncel and Pouransari, Hadi},
title = {Forward Compatible Training for Large-Scale Embedding Retrieval Systems},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vi... | In visual retrieval systems, updating the embedding model requires recomputing features for every piece of data. This expensive process is referred to as backfilling. Recently, the idea of backward compatible training (BCT) was proposed. To avoid the cost of backfilling, BCT modifies training of the new model to make i... | [
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117 | Everything at Once - Multi-Modal Fusion Transformer for Video Retrieval | [
"Nina Shvetsova",
"Brian Chen",
"Andrew Rouditchenko",
"Samuel Thomas",
"Brian Kingsbury",
"Rogerio S. Feris",
"David Harwath",
"James Glass",
"Hilde Kuehne"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Shvetsova_Everything_at_Once_-_Multi-Modal_Fusion_Transformer_for_Video_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Shvetsova_Everything_at_Once_-_Multi-Modal_Fusion_Transformer_for_Video_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shvetsova_Everything_at_Once_CVPR_2022_supplemental.pdf | 2112.04446 | cvf | @InProceedings{Shvetsova_2022_CVPR,
author = {Shvetsova, Nina and Chen, Brian and Rouditchenko, Andrew and Thomas, Samuel and Kingsbury, Brian and Feris, Rogerio S. and Harwath, David and Glass, James and Kuehne, Hilde},
title = {Everything at Once - Multi-Modal Fusion Transformer for Video Retrieval},
... | Multi-modal learning from video data has seen increased attention recently as it allows training of semantically meaningful embeddings without human annotation, enabling tasks like zero-shot retrieval and action localization. In this work, we present a multi-modal, modality agnostic fusion transformer that learns to ex... | [
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118 | Swin Transformer V2: Scaling Up Capacity and Resolution | [
"Ze Liu",
"Han Hu",
"Yutong Lin",
"Zhuliang Yao",
"Zhenda Xie",
"Yixuan Wei",
"Jia Ning",
"Yue Cao",
"Zheng Zhang",
"Li Dong",
"Furu Wei",
"Baining Guo"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Swin_Transformer_V2_Scaling_Up_Capacity_and_Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Swin_Transformer_V2_Scaling_Up_Capacity_and_Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Swin_Transformer_V2_CVPR_2022_supplemental.pdf | 2111.09883 | cvf | @InProceedings{Liu_2022_CVPR,
author = {Liu, Ze and Hu, Han and Lin, Yutong and Yao, Zhuliang and Xie, Zhenda and Wei, Yixuan and Ning, Jia and Cao, Yue and Zhang, Zheng and Dong, Li and Wei, Furu and Guo, Baining},
title = {Swin Transformer V2: Scaling Up Capacity and Resolution},
booktitle = {Proce... | We present techniques for scaling Swin Transformer [??] up to 3 billion parameters and making it capable of training with images of up to 1,536x1,536 resolution. By scaling up capacity and resolution, Swin Transformer sets new records on four representative vision benchmarks: 84.0% top-1 accuracy on ImageNet-V2 image c... | [
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119 | Neural Template: Topology-Aware Reconstruction and Disentangled Generation of 3D Meshes | [
"Ka-Hei Hui",
"Ruihui Li",
"Jingyu Hu",
"Chi-Wing Fu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Hui_Neural_Template_Topology-Aware_Reconstruction_and_Disentangled_Generation_of_3D_Meshes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Hui_Neural_Template_Topology-Aware_Reconstruction_and_Disentangled_Generation_of_3D_Meshes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hui_Neural_Template_Topology-Aware_CVPR_2022_supplemental.pdf | 2206.04942 | title_snapshot | @InProceedings{Hui_2022_CVPR,
author = {Hui, Ka-Hei and Li, Ruihui and Hu, Jingyu and Fu, Chi-Wing},
title = {Neural Template: Topology-Aware Reconstruction and Disentangled Generation of 3D Meshes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | This paper introduces a novel framework called DT-Net for 3D mesh reconstruction and generation via Disentangled Topology. Beyond previous works, we learn a topology-aware neural template specific to each input then deform the template to reconstruct a detailed mesh while preserving the learned topology. One key insigh... | [
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120 | DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints | [
"Zhendong Zhao",
"Xiaojun Chen",
"Yuexin Xuan",
"Ye Dong",
"Dakui Wang",
"Kaitai Liang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_DEFEAT_Deep_Hidden_Feature_Backdoor_Attacks_by_Imperceptible_Perturbation_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_DEFEAT_Deep_Hidden_Feature_Backdoor_Attacks_by_Imperceptible_Perturbation_and_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Zhao_2022_CVPR,
author = {Zhao, Zhendong and Chen, Xiaojun and Xuan, Yuexin and Dong, Ye and Wang, Dakui and Liang, Kaitai},
title = {DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints},
booktitle = {Proceedings of the IEEE/... | Backdoor attack is a type of serious security threat to deep learning models.An adversary can provide users with a model trained on poisoned data to manipulate prediction behavior in test stage using a backdoor. The backdoored models behave normally on clean images, yet can be activated and output incorrect prediction ... | [
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121 | Projective Manifold Gradient Layer for Deep Rotation Regression | [
"Jiayi Chen",
"Yingda Yin",
"Tolga Birdal",
"Baoquan Chen",
"Leonidas J. Guibas",
"He Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Projective_Manifold_Gradient_Layer_for_Deep_Rotation_Regression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Projective_Manifold_Gradient_CVPR_2022_supplemental.pdf | 2110.11657 | cvf | @InProceedings{Chen_2022_CVPR,
author = {Chen, Jiayi and Yin, Yingda and Birdal, Tolga and Chen, Baoquan and Guibas, Leonidas J. and Wang, He},
title = {Projective Manifold Gradient Layer for Deep Rotation Regression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | Regressing rotations on SO(3) manifold using deep neural networks is an important yet unsolved problem. The gap between the Euclidean network output space and the non-Euclidean SO(3) manifold imposes a severe challenge for neural network learning in both forward and backward passes. While several works have proposed di... | [
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122 | CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation | [
"Jinheng Xie",
"Xianxu Hou",
"Kai Ye",
"Linlin Shen"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_CLIMS_Cross_Language_Image_Matching_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_CLIMS_Cross_Language_Image_Matching_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_CLIMS_Cross_Language_CVPR_2022_supplemental.pdf | 2203.02668 | title_judge | @InProceedings{Xie_2022_CVPR,
author = {Xie, Jinheng and Hou, Xianxu and Ye, Kai and Shen, Linlin},
title = {CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | It has been widely known that CAM (Class Activation Map) usually only activates discriminative object regions and falsely includes lots of object-related backgrounds. As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to... | [
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123 | Learning To Refactor Action and Co-Occurrence Features for Temporal Action Localization | [
"Kun Xia",
"Le Wang",
"Sanping Zhou",
"Nanning Zheng",
"Wei Tang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xia_Learning_To_Refactor_CVPR_2022_supplemental.pdf | 2206.11493 | title_snapshot | @InProceedings{Xia_2022_CVPR,
author = {Xia, Kun and Wang, Le and Zhou, Sanping and Zheng, Nanning and Tang, Wei},
title = {Learning To Refactor Action and Co-Occurrence Features for Temporal Action Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e.g., context and background, in an untrimmed video. While prior approaches have achieved substantial progress through devising advanced action detectors, they still suffer from these co-occurri... | [
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124 | It's Time for Artistic Correspondence in Music and Video | [
"Dídac Surís",
"Carl Vondrick",
"Bryan Russell",
"Justin Salamon"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Suris_Its_Time_for_Artistic_Correspondence_in_Music_and_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Suris_Its_Time_for_Artistic_Correspondence_in_Music_and_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Suris_Its_Time_for_CVPR_2022_supplemental.pdf | 2206.07148 | title_snapshot | @InProceedings{Suris_2022_CVPR,
author = {Sur{\'\i}s, D{\'\i}dac and Vondrick, Carl and Russell, Bryan and Salamon, Justin},
title = {It's Time for Artistic Correspondence in Music and Video},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We present an approach for recommending a music track for a given video, and vice versa, based on both their temporal alignment and their correspondence at an artistic level. We propose a self-supervised approach that learns this correspondence directly from data, without any need of human annotations. In order to capt... | [
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125 | Mixed Differential Privacy in Computer Vision | [
"Aditya Golatkar",
"Alessandro Achille",
"Yu-Xiang Wang",
"Aaron Roth",
"Michael Kearns",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Golatkar_Mixed_Differential_Privacy_in_Computer_Vision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Golatkar_Mixed_Differential_Privacy_in_Computer_Vision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Golatkar_Mixed_Differential_Privacy_CVPR_2022_supplemental.pdf | 2203.11481 | cvf | @InProceedings{Golatkar_2022_CVPR,
author = {Golatkar, Aditya and Achille, Alessandro and Wang, Yu-Xiang and Roth, Aaron and Kearns, Michael and Soatto, Stefano},
title = {Mixed Differential Privacy in Computer Vision},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yie... | [
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126 | AdaFace: Quality Adaptive Margin for Face Recognition | [
"Minchul Kim",
"Anil K. Jain",
"Xiaoming Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_AdaFace_Quality_Adaptive_Margin_for_Face_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_AdaFace_Quality_Adaptive_Margin_for_Face_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_AdaFace_Quality_Adaptive_CVPR_2022_supplemental.pdf | 2204.00964 | cvf | @InProceedings{Kim_2022_CVPR,
author = {Kim, Minchul and Jain, Anil K. and Liu, Xiaoming},
title = {AdaFace: Quality Adaptive Margin for Face Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {... | Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded. Advances in margin-based loss functions have resulted in enhanced discriminability of faces in the embedding space. Further, previous studies have studied the effect of adaptive losses to assign more importance ... | [
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127 | Learning Soft Estimator of Keypoint Scale and Orientation With Probabilistic Covariant Loss | [
"Pei Yan",
"Yihua Tan",
"Shengzhou Xiong",
"Yuan Tai",
"Yansheng Li"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Learning_Soft_Estimator_of_Keypoint_Scale_and_Orientation_With_Probabilistic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Learning_Soft_Estimator_of_Keypoint_Scale_and_Orientation_With_Probabilistic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yan_Learning_Soft_Estimator_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Yan_2022_CVPR,
author = {Yan, Pei and Tan, Yihua and Xiong, Shengzhou and Tai, Yuan and Li, Yansheng},
title = {Learning Soft Estimator of Keypoint Scale and Orientation With Probabilistic Covariant Loss},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Estimating keypoint scale and orientation is crucial to extracting invariant features under significant geometric changes. Recently, the estimators based on self-supervised learning have been designed to adapt to complex imaging conditions. Such learning-based estimators generally predict a single scalar for the keypoi... | [
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128 | DN-DETR: Accelerate DETR Training by Introducing Query DeNoising | [
"Feng Li",
"Hao Zhang",
"Shilong Liu",
"Jian Guo",
"Lionel M. Ni",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_DN-DETR_Accelerate_DETR_Training_by_Introducing_Query_DeNoising_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_DN-DETR_Accelerate_DETR_Training_by_Introducing_Query_DeNoising_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_DN-DETR_Accelerate_DETR_CVPR_2022_supplemental.pdf | 2203.01305 | title_snapshot | @InProceedings{Li_2022_CVPR,
author = {Li, Feng and Zhang, Hao and Liu, Shilong and Guo, Jian and Ni, Lionel M. and Zhang, Lei},
title = {DN-DETR: Accelerate DETR Training by Introducing Query DeNoising},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimiza... | [
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129 | HCSC: Hierarchical Contrastive Selective Coding | [
"Yuanfan Guo",
"Minghao Xu",
"Jiawen Li",
"Bingbing Ni",
"Xuanyu Zhu",
"Zhenbang Sun",
"Yi Xu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_HCSC_Hierarchical_Contrastive_Selective_Coding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_HCSC_Hierarchical_Contrastive_Selective_Coding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_HCSC_Hierarchical_Contrastive_CVPR_2022_supplemental.pdf | 2202.00455 | cvf | @InProceedings{Guo_2022_CVPR,
author = {Guo, Yuanfan and Xu, Minghao and Li, Jiawen and Ni, Bingbing and Zhu, Xuanyu and Sun, Zhenbang and Xu, Yi},
title = {HCSC: Hierarchical Contrastive Selective Coding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coarser-grained semantics. Capturing such structures with image representations can greatly benefit the semantic understanding on various downs... | [
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130 | TransRank: Self-Supervised Video Representation Learning via Ranking-Based Transformation Recognition | [
"Haodong Duan",
"Nanxuan Zhao",
"Kai Chen",
"Dahua Lin"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Duan_TransRank_Self-Supervised_Video_Representation_Learning_via_Ranking-Based_Transformation_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Duan_TransRank_Self-Supervised_Video_CVPR_2022_supplemental.pdf | 2205.02028 | cvf | @InProceedings{Duan_2022_CVPR,
author = {Duan, Haodong and Zhao, Nanxuan and Chen, Kai and Lin, Dahua},
title = {TransRank: Self-Supervised Video Representation Learning via Ranking-Based Transformation Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | Recognizing transformation types applied to a video clip (RecogTrans) is a long-established paradigm for self-supervised video representation learning, which achieves much inferior performance compared to instance discrimination approaches (InstDisc) in recent works. However, based on a thorough comparison of represent... | [
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131 | KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos | [
"David Novotny",
"Ignacio Rocco",
"Samarth Sinha",
"Alexandre Carlier",
"Gael Kerchenbaum",
"Roman Shapovalov",
"Nikita Smetanin",
"Natalia Neverova",
"Benjamin Graham",
"Andrea Vedaldi"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Novotny_KeyTr_Keypoint_Transporter_for_3D_Reconstruction_of_Deformable_Objects_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Novotny_KeyTr_Keypoint_Transporter_for_3D_Reconstruction_of_Deformable_Objects_in_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Novotny_2022_CVPR,
author = {Novotny, David and Rocco, Ignacio and Sinha, Samarth and Carlier, Alexandre and Kerchenbaum, Gael and Shapovalov, Roman and Smetanin, Nikita and Neverova, Natalia and Graham, Benjamin and Vedaldi, Andrea},
title = {KeyTr: Keypoint Transporter for 3D Reconstruct... | We consider the problem of reconstructing the depth of dynamic objects from videos. Recent progress in dynamic video depth prediction has focused on improving the output of monocular depth estimators by means of multi-view constraints while imposing little to no restrictions on the deformation of the dynamic parts of t... | [
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132 | Invariant Grounding for Video Question Answering | [
"Yicong Li",
"Xiang Wang",
"Junbin Xiao",
"Wei Ji",
"Tat-Seng Chua"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Invariant_Grounding_for_Video_Question_Answering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Invariant_Grounding_for_CVPR_2022_supplemental.pdf | 2206.02349 | title_snapshot | @InProceedings{Li_2022_CVPR,
author = {Li, Yicong and Wang, Xiang and Xiao, Junbin and Ji, Wei and Chua, Tat-Seng},
title = {Invariant Grounding for Video Question Answering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June... | Video Question Answering (VideoQA) is the task of answering questions about a video. At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer. In leading VideoQA models, the typical learning objective, empirical risk minimization (ERM), latches ... | [
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133 | Prompt Distribution Learning | [
"Yuning Lu",
"Jianzhuang Liu",
"Yonggang Zhang",
"Yajing Liu",
"Xinmei Tian"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Prompt_Distribution_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Prompt_Distribution_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Prompt_Distribution_Learning_CVPR_2022_supplemental.pdf | 2205.03340 | cvf | @InProceedings{Lu_2022_CVPR,
author = {Lu, Yuning and Liu, Jianzhuang and Zhang, Yonggang and Liu, Yajing and Tian, Xinmei},
title = {Prompt Distribution Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year... | We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias prompts from a few samples but also captures the distribution of diverse prompts to handle the varying visual representations. In this way, we... | [
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134 | RAGO: Recurrent Graph Optimizer for Multiple Rotation Averaging | [
"Heng Li",
"Zhaopeng Cui",
"Shuaicheng Liu",
"Ping Tan"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_RAGO_Recurrent_Graph_Optimizer_for_Multiple_Rotation_Averaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_RAGO_Recurrent_Graph_Optimizer_for_Multiple_Rotation_Averaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_RAGO_Recurrent_Graph_CVPR_2022_supplemental.pdf | 2212.07211 | title_snapshot | @InProceedings{Li_2022_CVPR,
author = {Li, Heng and Cui, Zhaopeng and Liu, Shuaicheng and Tan, Ping},
title = {RAGO: Recurrent Graph Optimizer for Multiple Rotation Averaging},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | This paper proposes a deep recurrent Rotation Averaging Graph Optimizer (RAGO) for Multiple Rotation Averaging (MRA). Conventional optimization-based methods usually fail to produce accurate results due to corrupted and noisy relative measurements. Recent learning-based approaches regard MRA as a regression problem, wh... | [
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135 | Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search | [
"Minbin Huang",
"Zhijian Huang",
"Changlin Li",
"Xin Chen",
"Hang Xu",
"Zhenguo Li",
"Xiaodan Liang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Arch-Graph_Acyclic_Architecture_Relation_Predictor_for_Task-Transferable_Neural_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Arch-Graph_Acyclic_Architecture_Relation_Predictor_for_Task-Transferable_Neural_Architecture_Search_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Arch-Graph_Acyclic_Architecture_CVPR_2022_supplemental.pdf | 2204.05941 | title_snapshot | @InProceedings{Huang_2022_CVPR,
author = {Huang, Minbin and Huang, Zhijian and Li, Changlin and Chen, Xin and Xu, Hang and Li, Zhenguo and Liang, Xiaodan},
title = {Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search},
booktitle = {Proceedings of the I... | Neural Architecture Search (NAS) aims to find efficient models for multiple tasks. Beyond seeking solutions for a single task, there are surging interests in transferring network design knowledge across multiple tasks. In this line of research, effectively modeling task correlations is vital yet highly neglected. There... | [
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136 | On Aliased Resizing and Surprising Subtleties in GAN Evaluation | [
"Gaurav Parmar",
"Richard Zhang",
"Jun-Yan Zhu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Parmar_On_Aliased_Resizing_and_Surprising_Subtleties_in_GAN_Evaluation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Parmar_On_Aliased_Resizing_and_Surprising_Subtleties_in_GAN_Evaluation_CVPR_2022_paper.pdf | null | 2104.11222 | cvf | @InProceedings{Parmar_2022_CVPR,
author = {Parmar, Gaurav and Zhang, Richard and Zhu, Jun-Yan},
title = {On Aliased Resizing and Surprising Subtleties in GAN Evaluation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Metrics for evaluating generative models aim to measure the discrepancy between real and generated images. The oftenused Frechet Inception Distance (FID) metric, for example, extracts "high-level" features using a deep network from the two sets. However, we find that the differences in "low-level" preprocessing, specif... | [
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137 | Lepard: Learning Partial Point Cloud Matching in Rigid and Deformable Scenes | [
"Yang Li",
"Tatsuya Harada"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Lepard_Learning_Partial_Point_Cloud_Matching_in_Rigid_and_Deformable_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Lepard_Learning_Partial_CVPR_2022_supplemental.pdf | 2111.12591 | cvf | @InProceedings{Li_2022_CVPR,
author = {Li, Yang and Harada, Tatsuya},
title = {Lepard: Learning Partial Point Cloud Matching in Rigid and Deformable Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year =... | We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes. The key characteristics are the following techniques that exploit 3D positional knowledge for point cloud matching: 1) An architecture that disentangles point cloud representation into feature space and 3D posi... | [
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138 | Virtual Elastic Objects | [
"Hsiao-yu Chen",
"Edith Tretschk",
"Tuur Stuyck",
"Petr Kadlecek",
"Ladislav Kavan",
"Etienne Vouga",
"Christoph Lassner"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Virtual_Elastic_Objects_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Virtual_Elastic_Objects_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Virtual_Elastic_Objects_CVPR_2022_supplemental.pdf | 2201.04623 | cvf | @InProceedings{Chen_2022_CVPR,
author = {Chen, Hsiao-yu and Tretschk, Edith and Stuyck, Tuur and Kadlecek, Petr and Kavan, Ladislav and Vouga, Etienne and Lassner, Christoph},
title = {Virtual Elastic Objects},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions. Achieving this presents multiple challenges: not only do objects have to be captured including the physical forces acting on them, then fai... | [
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139 | DiSparse: Disentangled Sparsification for Multitask Model Compression | [
"Xinglong Sun",
"Ali Hassani",
"Zhangyang Wang",
"Gao Huang",
"Humphrey Shi"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_DiSparse_Disentangled_Sparsification_for_Multitask_Model_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_DiSparse_Disentangled_Sparsification_CVPR_2022_supplemental.pdf | 2206.04662 | title_snapshot | @InProceedings{Sun_2022_CVPR,
author = {Sun, Xinglong and Hassani, Ali and Wang, Zhangyang and Huang, Gao and Shi, Humphrey},
title = {DiSparse: Disentangled Sparsification for Multitask Model Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglement of tasks in the parameter space. In this paper, we propose DiSparse, a simple, effective, and first-of-its-kind multitask pruning and sp... | [
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140 | Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference | [
"Shell Xu Hu",
"Da Li",
"Jan Stühmer",
"Minyoung Kim",
"Timothy M. Hospedales"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Pushing_the_Limits_of_Simple_Pipelines_for_Few-Shot_Learning_External_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Pushing_the_Limits_CVPR_2022_supplemental.pdf | 2204.07305 | title_snapshot | @InProceedings{Hu_2022_CVPR,
author = {Hu, Shell Xu and Li, Da and St\"uhmer, Jan and Kim, Minyoung and Hospedales, Timothy M.},
title = {Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference},
booktitle = {Proceedings of the IEEE/CVF Conference... | Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated meta-learning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for real-world few-shot image ... | [
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141 | Opening Up Open World Tracking | [
"Yang Liu",
"Idil Esen Zulfikar",
"Jonathon Luiten",
"Achal Dave",
"Deva Ramanan",
"Bastian Leibe",
"Aljoša Ošep",
"Laura Leal-Taixé"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Opening_Up_Open_World_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Opening_Up_Open_World_Tracking_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Opening_Up_Open_CVPR_2022_supplemental.pdf | 2104.11221 | cvf | @InProceedings{Liu_2022_CVPR,
author = {Liu, Yang and Zulfikar, Idil Esen and Luiten, Jonathon and Dave, Achal and Ramanan, Deva and Leibe, Bastian and O\v{s}ep, Aljo\v{s}a and Leal-Taix\'e, Laura},
title = {Opening Up Open World Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Compu... | Tracking and detecting any object, including ones never-seen-before during model training, is a crucial but elusive capability of autonomous systems. An autonomous agent that is blind to never-seen-before objects poses a safety hazard when operating in the real world - and yet this is how almost all current systems wor... | [
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142 | Towards Efficient and Scalable Sharpness-Aware Minimization | [
"Yong Liu",
"Siqi Mai",
"Xiangning Chen",
"Cho-Jui Hsieh",
"Yang You"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Efficient_and_Scalable_Sharpness-Aware_Minimization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Towards_Efficient_and_Scalable_Sharpness-Aware_Minimization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Towards_Efficient_and_CVPR_2022_supplemental.pdf | 2203.02714 | cvf | @InProceedings{Liu_2022_CVPR,
author = {Liu, Yong and Mai, Siqi and Chen, Xiangning and Hsieh, Cho-Jui and You, Yang},
title = {Towards Efficient and Scalable Sharpness-Aware Minimization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of the loss landscape and generalization, has demonstrated a significant performance boost on training large-scale models such as vision transformers. However, the update rule of SAM requires two sequential (non-parallelizable) gradient computati... | [
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143 | VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention | [
"Shengheng Deng",
"Zhihao Liang",
"Lin Sun",
"Kui Jia"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_VISTA_Boosting_3D_Object_Detection_via_Dual_Cross-VIew_SpaTial_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Deng_VISTA_Boosting_3D_Object_Detection_via_Dual_Cross-VIew_SpaTial_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Deng_VISTA_Boosting_3D_CVPR_2022_supplemental.pdf | 2203.09704 | cvf | @InProceedings{Deng_2022_CVPR,
author = {Deng, Shengheng and Liang, Zhihao and Sun, Lin and Jia, Kui},
title = {VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved due to the sparsity and irregularity of LiDAR point clouds. Among existing strategies, multi-view methods have shown great promise by leverag... | [
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144 | Rethinking Deep Face Restoration | [
"Yang Zhao",
"Yu-Chuan Su",
"Chun-Te Chu",
"Yandong Li",
"Marius Renn",
"Yukun Zhu",
"Changyou Chen",
"Xuhui Jia"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Rethinking_Deep_Face_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Rethinking_Deep_Face_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Zhao_2022_CVPR,
author = {Zhao, Yang and Su, Yu-Chuan and Chu, Chun-Te and Li, Yandong and Renn, Marius and Zhu, Yukun and Chen, Changyou and Jia, Xuhui},
title = {Rethinking Deep Face Restoration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | A model that can authentically restore a low-quality face image to a high-quality one can benefit many applications. While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces. Beca... | [
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145 | OSSO: Obtaining Skeletal Shape From Outside | [
"Marilyn Keller",
"Silvia Zuffi",
"Michael J. Black",
"Sergi Pujades"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Keller_OSSO_Obtaining_Skeletal_Shape_From_Outside_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Keller_OSSO_Obtaining_Skeletal_Shape_From_Outside_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Keller_OSSO_Obtaining_Skeletal_CVPR_2022_supplemental.pdf | 2204.10129 | cvf | @InProceedings{Keller_2022_CVPR,
author = {Keller, Marilyn and Zuffi, Silvia and Black, Michael J. and Pujades, Sergi},
title = {OSSO: Obtaining Skeletal Shape From Outside},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | We address the problem of inferring the anatomic skeleton of a person, in an arbitrary pose, from the 3D surface of the body; i.e. we predict the inside (bones) from the outside (skin). This has many applications in medicine and biomechanics. Existing state-of-the-art biomechanical skeletons are detailed but do not eas... | [
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146 | Temporal Alignment Networks for Long-Term Video | [
"Tengda Han",
"Weidi Xie",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Temporal_Alignment_Networks_for_Long-Term_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Temporal_Alignment_Networks_for_Long-Term_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Temporal_Alignment_Networks_CVPR_2022_supplemental.zip | 2204.02968 | cvf | @InProceedings{Han_2022_CVPR,
author = {Han, Tengda and Xie, Weidi and Zisserman, Andrew},
title = {Temporal Alignment Networks for Long-Term Video},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},... | The objective of this paper is a temporal alignment network that ingests long term video sequences, and associated text sentences, in order to: (1) determine if a sentence is alignable with the video; and (2) if it is alignable, then determine its alignment. The challenge is to train such networks from large-scale data... | [
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147 | Few-Shot Head Swapping in the Wild | [
"Changyong Shu",
"Hemao Wu",
"Hang Zhou",
"Jiaming Liu",
"Zhibin Hong",
"Changxing Ding",
"Junyu Han",
"Jingtuo Liu",
"Errui Ding",
"Jingdong Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Shu_Few-Shot_Head_Swapping_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Shu_Few-Shot_Head_Swapping_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shu_Few-Shot_Head_Swapping_CVPR_2022_supplemental.zip | 2204.13100 | cvf | @InProceedings{Shu_2022_CVPR,
author = {Shu, Changyong and Wu, Hemao and Zhou, Hang and Liu, Jiaming and Hong, Zhibin and Ding, Changxing and Han, Junyu and Liu, Jingtuo and Ding, Errui and Wang, Jingdong},
title = {Few-Shot Head Swapping in the Wild},
booktitle = {Proceedings of the IEEE/CVF Confere... | The head swapping task aims at flawlessly placing a source head onto a target body, which is of great importance to various entertainment scenarios. While face swapping has drawn much attention in the community, the task of head swapping has rarely been explored, particularly under the few-shot setting. It is inherentl... | [
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148 | A Study on the Distribution of Social Biases in Self-Supervised Learning Visual Models | [
"Kirill Sirotkin",
"Pablo Carballeira",
"Marcos Escudero-Viñolo"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Sirotkin_A_Study_on_the_Distribution_of_Social_Biases_in_Self-Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Sirotkin_A_Study_on_the_Distribution_of_Social_Biases_in_Self-Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sirotkin_A_Study_on_CVPR_2022_supplemental.pdf | 2203.01854 | title_snapshot | @InProceedings{Sirotkin_2022_CVPR,
author = {Sirotkin, Kirill and Carballeira, Pablo and Escudero-Vi\~nolo, Marcos},
title = {A Study on the Distribution of Social Biases in Self-Supervised Learning Visual Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | Deep neural networks are efficient at learning the data distribution if it is sufficiently sampled. However, they can be strongly biased by non-relevant factors implicitly incorporated in the training data. These include operational biases, such as ineffective or uneven data sampling, but also ethical concerns, as the ... | [
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149 | LAR-SR: A Local Autoregressive Model for Image Super-Resolution | [
"Baisong Guo",
"Xiaoyun Zhang",
"Haoning Wu",
"Yu Wang",
"Ya Zhang",
"Yan-Feng Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_LAR-SR_A_Local_Autoregressive_Model_for_Image_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_LAR-SR_A_Local_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Guo_2022_CVPR,
author = {Guo, Baisong and Zhang, Xiaoyun and Wu, Haoning and Wang, Yu and Zhang, Ya and Wang, Yan-Feng},
title = {LAR-SR: A Local Autoregressive Model for Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output. Recent works combine adversarial loss with pixel-wise loss to train a GAN-based model or introduce normalizing flows into SR problems to generate more realistic ... | [
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150 | Bayesian Invariant Risk Minimization | [
"Yong Lin",
"Hanze Dong",
"Hao Wang",
"Tong Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Bayesian_Invariant_Risk_Minimization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Bayesian_Invariant_Risk_Minimization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Bayesian_Invariant_Risk_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Lin_2022_CVPR,
author = {Lin, Yong and Dong, Hanze and Wang, Hao and Zhang, Tong},
title = {Bayesian Invariant Risk Minimization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
... | Generalization under distributional shift is an open challenge for machine learning. Invariant Risk Minimization (IRM) is a promising framework to tackle this issue by extracting invariant features. However, despite the potential and popularity of IRM, recent works have reported negative results of it on deep models. W... | [
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151 | Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection | [
"Siyue Yu",
"Jimin Xiao",
"Bingfeng Zhang",
"Eng Gee Lim"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Democracy_Does_Matter_Comprehensive_Feature_Mining_for_Co-Salient_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Democracy_Does_Matter_Comprehensive_Feature_Mining_for_Co-Salient_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Democracy_Does_Matter_CVPR_2022_supplemental.pdf | 2203.05787 | cvf | @InProceedings{Yu_2022_CVPR,
author = {Yu, Siyue and Xiao, Jimin and Zhang, Bingfeng and Lim, Eng Gee},
title = {Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Co-salient object detection, with the target of detecting co-existed salient objects among a group of images, is gaining popularity. Recent works use the attention mechanism or extra information to aggregate common co-salient features, leading to incomplete even incorrect responses for target objects. In this paper, we... | [
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152 | Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint | [
"Jiaxian Guo",
"Jiachen Li",
"Huan Fu",
"Mingming Gong",
"Kun Zhang",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Alleviating_Semantics_Distortion_in_Unsupervised_Low-Level_Image-to-Image_Translation_via_Structure_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Alleviating_Semantics_Distortion_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Guo_2022_CVPR,
author = {Guo, Jiaxian and Li, Jiachen and Fu, Huan and Gong, Mingming and Zhang, Kun and Tao, Dacheng},
title = {Alleviating Semantics Distortion in Unsupervised Low-Level Image-to-Image Translation via Structure Consistency Constraint},
booktitle = {Proceedings of the ... | Unsupervised image-to-image (I2I) translation aims to learn a ___domain mapping function that can preserve the semantics of the input images without paired data. However, because the underlying semantics distributions in the source and target domains are often mismatched, current distribution matching-based methods may di... | [
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153 | Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches | [
"Ayan Kumar Bhunia",
"Viswanatha Reddy Gajjala",
"Subhadeep Koley",
"Rohit Kundu",
"Aneeshan Sain",
"Tao Xiang",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Bhunia_Doodle_It_Yourself_Class_Incremental_Learning_by_Drawing_a_Few_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Bhunia_Doodle_It_Yourself_Class_Incremental_Learning_by_Drawing_a_Few_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bhunia_Doodle_It_Yourself_CVPR_2022_supplemental.pdf | 2203.14843 | cvf | @InProceedings{Bhunia_2022_CVPR,
author = {Bhunia, Ayan Kumar and Gajjala, Viswanatha Reddy and Koley, Subhadeep and Kundu, Rohit and Sain, Aneeshan and Xiang, Tao and Song, Yi-Zhe},
title = {Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches},
booktitle = {Proceedings of the IE... | The human visual system is remarkable in learning new visual concepts from just a few examples. This is precisely the goal behind few-shot class incremental learning (FSCIL), where the emphasis is additionally placed on ensuring the model does not suffer from "forgetting". In this paper, we push the boundary further fo... | [
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154 | ICON: Implicit Clothed Humans Obtained From Normals | [
"Yuliang Xiu",
"Jinlong Yang",
"Dimitrios Tzionas",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xiu_ICON_Implicit_Clothed_CVPR_2022_supplemental.pdf | 2112.09127 | cvf | @InProceedings{Xiu_2022_CVPR,
author = {Xiu, Yuliang and Yang, Jinlong and Tzionas, Dimitrios and Black, Michael J.},
title = {ICON: Implicit Clothed Humans Obtained From Normals},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | Current methods for learning realistic and animatable 3D clothed avatars need either posed 3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn the avatar from only 2D images of people in unconstrained poses. Given a set of images, our method estimates a detailed 3D surface from... | [
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155 | Comparing Correspondences: Video Prediction With Correspondence-Wise Losses | [
"Daniel Geng",
"Max Hamilton",
"Andrew Owens"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Geng_Comparing_Correspondences_Video_Prediction_With_Correspondence-Wise_Losses_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Geng_Comparing_Correspondences_Video_Prediction_With_Correspondence-Wise_Losses_CVPR_2022_paper.pdf | null | 2104.09498 | cvf | @InProceedings{Geng_2022_CVPR,
author = {Geng, Daniel and Hamilton, Max and Owens, Andrew},
title = {Comparing Correspondences: Video Prediction With Correspondence-Wise Losses},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Image prediction methods often struggle on tasks that require changing the positions of objects, such as video prediction, producing blurry images that average over the many positions that objects might occupy. In this paper, we propose a simple change to existing image similarity metrics that makes them more robust to... | [
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156 | Uni-Perceiver: Pre-Training Unified Architecture for Generic Perception for Zero-Shot and Few-Shot Tasks | [
"Xizhou Zhu",
"Jinguo Zhu",
"Hao Li",
"Xiaoshi Wu",
"Hongsheng Li",
"Xiaohua Wang",
"Jifeng Dai"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Uni-Perceiver_Pre-Training_Unified_Architecture_for_Generic_Perception_for_Zero-Shot_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Uni-Perceiver_Pre-Training_Unified_CVPR_2022_supplemental.pdf | 2112.01522 | title_snapshot | @InProceedings{Zhu_2022_CVPR,
author = {Zhu, Xizhou and Zhu, Jinguo and Li, Hao and Wu, Xiaoshi and Li, Hongsheng and Wang, Xiaohua and Dai, Jifeng},
title = {Uni-Perceiver: Pre-Training Unified Architecture for Generic Perception for Zero-Shot and Few-Shot Tasks},
booktitle = {Proceedings of the IEE... | Biological intelligence systems of animals perceive the world by integrating information in different modalities and processing simultaneously for various tasks. In contrast, current machine learning research follows a task-specific paradigm, leading to inefficient collaboration between tasks and high marginal costs of... | [
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157 | The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift | [
"Sara Beery",
"Guanhang Wu",
"Trevor Edwards",
"Filip Pavetic",
"Bo Majewski",
"Shreyasee Mukherjee",
"Stanley Chan",
"John Morgan",
"Vivek Rathod",
"Jonathan Huang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Beery_The_Auto_Arborist_Dataset_A_Large-Scale_Benchmark_for_Multiview_Urban_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Beery_The_Auto_Arborist_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Beery_2022_CVPR,
author = {Beery, Sara and Wu, Guanhang and Edwards, Trevor and Pavetic, Filip and Majewski, Bo and Mukherjee, Shreyasee and Chan, Stanley and Morgan, John and Rathod, Vivek and Huang, Jonathan},
title = {The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urba... | Generalization to novel domains is a fundamental challenge for computer vision. Near-perfect accuracy on benchmarks is common, but these models do not work as expected when deployed outside of the training distribution. To build computer vision systems that truly solve real-world problems at global scale, we need bench... | [
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158 | On the Instability of Relative Pose Estimation and RANSAC's Role | [
"Hongyi Fan",
"Joe Kileel",
"Benjamin Kimia"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_On_the_Instability_of_Relative_Pose_Estimation_and_RANSACs_Role_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_On_the_Instability_of_Relative_Pose_Estimation_and_RANSACs_Role_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_On_the_Instability_CVPR_2022_supplemental.pdf | 2112.14651 | title_snapshot | @InProceedings{Fan_2022_CVPR,
author = {Fan, Hongyi and Kileel, Joe and Kimia, Benjamin},
title = {On the Instability of Relative Pose Estimation and RANSAC's Role},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Relative pose estimation using the 5-point or 7-point Random Sample Consensus (RANSAC) algorithms can fail even when no outliers are present and there are enough inliers to support a hypothesis. These cases arise due to numerical instability of the 5- and 7-point minimal problems. This paper characterizes these instabi... | [
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159 | Shape From Polarization for Complex Scenes in the Wild | [
"Chenyang Lei",
"Chenyang Qi",
"Jiaxin Xie",
"Na Fan",
"Vladlen Koltun",
"Qifeng Chen"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_Shape_From_Polarization_for_Complex_Scenes_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Lei_Shape_From_Polarization_for_Complex_Scenes_in_the_Wild_CVPR_2022_paper.pdf | null | 2112.11377 | cvf | @InProceedings{Lei_2022_CVPR,
author = {Lei, Chenyang and Qi, Chenyang and Xie, Jiaxin and Fan, Na and Koltun, Vladlen and Chen, Qifeng},
title = {Shape From Polarization for Complex Scenes in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | We present a new data-driven approach with physics-based priors to scene-level normal estimation from a single polarization image. Existing shape from polarization (SfP) works mainly focus on estimating the normal of a single object rather than complex scenes in the wild. A key barrier to high-quality scene-level SfP i... | [
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160 | Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device | [
"Hyojin Park",
"Alan Yessenbayev",
"Tushar Singhal",
"Navin Kumar Adhikari",
"Yizhe Zhang",
"Shubhankar Mangesh Borse",
"Hong Cai",
"Nilesh Prasad Pandey",
"Fei Yin",
"Frank Mayer",
"Balaji Calidas",
"Fatih Porikli"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Real-Time_Accurate_and_Consistent_Video_Semantic_Segmentation_via_Unsupervised_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Real-Time_Accurate_and_Consistent_Video_Semantic_Segmentation_via_Unsupervised_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Real-Time_Accurate_and_CVPR_2022_supplemental.zip | null | null | @InProceedings{Park_2022_CVPR,
author = {Park, Hyojin and Yessenbayev, Alan and Singhal, Tushar and Adhikari, Navin Kumar and Zhang, Yizhe and Borse, Shubhankar Mangesh and Cai, Hong and Pandey, Nilesh Prasad and Yin, Fei and Mayer, Frank and Calidas, Balaji and Porikli, Fatih},
title = {Real-Time, Accur... | This demonstration showcases our innovations on efficient, accurate, and temporally consistent video semantic segmentation on mobile device. We employ our test-time unsupervised scheme, AuxAdapt, to enable the segmentation model to adapt to a given video in an online manner. More specifically, we leverage a small auxil... | [
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161 | SNUG: Self-Supervised Neural Dynamic Garments | [
"Igor Santesteban",
"Miguel A. Otaduy",
"Dan Casas"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Santesteban_SNUG_Self-Supervised_Neural_Dynamic_Garments_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Santesteban_SNUG_Self-Supervised_Neural_Dynamic_Garments_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Santesteban_SNUG_Self-Supervised_Neural_CVPR_2022_supplemental.zip | 2204.02219 | cvf | @InProceedings{Santesteban_2022_CVPR,
author = {Santesteban, Igor and Otaduy, Miguel A. and Casas, Dan},
title = {SNUG: Self-Supervised Neural Dynamic Garments},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We present a self-supervised method to learn dynamic 3D deformations of garments worn by parametric human bodies. State-of-the-art data-driven approaches to model 3D garment deformations are trained using supervised strategies that require large datasets, usually obtained by expensive physics-based simulation methods o... | [
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162 | Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation | [
"Binhui Xie",
"Longhui Yuan",
"Shuang Li",
"Chi Harold Liu",
"Xinjing Cheng"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Towards_Fewer_Annotations_Active_Learning_via_Region_Impurity_and_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Towards_Fewer_Annotations_CVPR_2022_supplemental.pdf | 2111.12940 | cvf | @InProceedings{Xie_2022_CVPR,
author = {Xie, Binhui and Yuan, Longhui and Li, Shuang and Liu, Chi Harold and Cheng, Xinjing},
title = {Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation},
booktitle = {Proceedings of the ... | Self-training has greatly facilitated ___domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network. However, realistic segmentation datasets are highly imbalanced, pseudo labels are typically biased to the majority classes and basically noisy, leadin... | [
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163 | Glass Segmentation Using Intensity and Spectral Polarization Cues | [
"Haiyang Mei",
"Bo Dong",
"Wen Dong",
"Jiaxi Yang",
"Seung-Hwan Baek",
"Felix Heide",
"Pieter Peers",
"Xiaopeng Wei",
"Xin Yang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mei_Glass_Segmentation_Using_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Mei_2022_CVPR,
author = {Mei, Haiyang and Dong, Bo and Dong, Wen and Yang, Jiaxi and Baek, Seung-Hwan and Heide, Felix and Peers, Pieter and Wei, Xiaopeng and Yang, Xin},
title = {Glass Segmentation Using Intensity and Spectral Polarization Cues},
booktitle = {Proceedings of the IEEE/C... | Transparent and semi-transparent materials pose significant challenges for existing scene understanding and segmentation algorithms due to their lack of RGB texture which impedes the extraction of meaningful features. In this work, we exploit that the light-matter interactions on glass materials provide unique intensit... | [
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164 | CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding | [
"Mohamed Afham",
"Isuru Dissanayake",
"Dinithi Dissanayake",
"Amaya Dharmasiri",
"Kanchana Thilakarathna",
"Ranga Rodrigo"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.pdf | null | 2203.00680 | cvf | @InProceedings{Afham_2022_CVPR,
author = {Afham, Mohamed and Dissanayake, Isuru and Dissanayake, Dinithi and Dharmasiri, Amaya and Thilakarathna, Kanchana and Rodrigo, Ranga},
title = {CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding},
booktitle = {Proceed... | Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds. Self-supervised learning, which operates without any human labeling, is a promising approach to address this issue. We ... | [
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165 | Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment | [
"Jiayu Xiao",
"Liang Li",
"Chaofei Wang",
"Zheng-Jun Zha",
"Qingming Huang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xiao_Few_Shot_Generative_Model_Adaption_via_Relaxed_Spatial_Structural_Alignment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiao_Few_Shot_Generative_Model_Adaption_via_Relaxed_Spatial_Structural_Alignment_CVPR_2022_paper.pdf | null | 2203.04121 | cvf | @InProceedings{Xiao_2022_CVPR,
author = {Xiao, Jiayu and Li, Liang and Wang, Chaofei and Zha, Zheng-Jun and Huang, Qingming},
title = {Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source ___domain and adapt it to the target ___domain with a few samples, termed as few shot generative model adaption. However, existing methods are prone to... | [
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166 | Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection | [
"Jiaxi Wu",
"Jiaxin Chen",
"Mengzhe He",
"Yiru Wang",
"Bo Li",
"Bingqi Ma",
"Weihao Gan",
"Wei Wu",
"Yali Wang",
"Di Huang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Target-Relevant_Knowledge_Preservation_for_Multi-Source_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Target-Relevant_Knowledge_Preservation_for_Multi-Source_Domain_Adaptive_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Target-Relevant_Knowledge_Preservation_CVPR_2022_supplemental.pdf | 2204.07964 | cvf | @InProceedings{Wu_2022_CVPR,
author = {Wu, Jiaxi and Chen, Jiaxin and He, Mengzhe and Wang, Yiru and Li, Bo and Ma, Bingqi and Gan, Weihao and Wu, Wei and Wang, Yali and Huang, Di},
title = {Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection},
booktitle = {Procee... | Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source ___domain adaptation, a more generalized task with multiple source domains remains not being well explored, due to knowledge degradation during their combination. T... | [
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167 | Pyramid Grafting Network for One-Stage High Resolution Saliency Detection | [
"Chenxi Xie",
"Changqun Xia",
"Mingcan Ma",
"Zhirui Zhao",
"Xiaowu Chen",
"Jia Li"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Pyramid_Grafting_Network_for_One-Stage_High_Resolution_Saliency_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Pyramid_Grafting_Network_for_One-Stage_High_Resolution_Saliency_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Pyramid_Grafting_Network_CVPR_2022_supplemental.pdf | 2204.05041 | cvf | @InProceedings{Xie_2022_CVPR,
author = {Xie, Chenxi and Xia, Changqun and Ma, Mingcan and Zhao, Zhirui and Chen, Xiaowu and Li, Jia},
title = {Pyramid Grafting Network for One-Stage High Resolution Saliency Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern... | Recent salient object detection (SOD) methods based on deep neural network have achieved remarkable performance. However, most of existing SOD models designed for low-resolution input perform poorly on high-resolution images due to the contradiction between the sampling depth and the receptive field size. Aiming at res... | [
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168 | A Style-Aware Discriminator for Controllable Image Translation | [
"Kunhee Kim",
"Sanghun Park",
"Eunyeong Jeon",
"Taehun Kim",
"Daijin Kim"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_A_Style-Aware_Discriminator_for_Controllable_Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_A_Style-Aware_Discriminator_for_Controllable_Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_A_Style-Aware_Discriminator_CVPR_2022_supplemental.pdf | 2203.15375 | cvf | @InProceedings{Kim_2022_CVPR,
author = {Kim, Kunhee and Park, Sanghun and Jeon, Eunyeong and Kim, Taehun and Kim, Daijin},
title = {A Style-Aware Discriminator for Controllable Image Translation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Current image-to-image translations do not control the output ___domain beyond the classes used during training, nor do they interpolate between different domains well, leading to implausible results. This limitation largely arises because labels do not consider the semantic distance. To mitigate such problems, we propose... | [
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169 | Non-Iterative Recovery From Nonlinear Observations Using Generative Models | [
"Jiulong Liu",
"Zhaoqiang Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Non-Iterative_Recovery_From_Nonlinear_Observations_Using_Generative_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Non-Iterative_Recovery_From_Nonlinear_Observations_Using_Generative_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Non-Iterative_Recovery_From_CVPR_2022_supplemental.zip | 2205.15749 | cvf | @InProceedings{Liu_2022_CVPR,
author = {Liu, Jiulong and Liu, Zhaoqiang},
title = {Non-Iterative Recovery From Nonlinear Observations Using Generative Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | In this paper, we aim to estimate the direction of an underlying signal from its nonlinear observations following the semi-parametric single index model (SIM). Unlike for conventional compressed sensing where the signal is assumed to be sparse, we assume that the signal lies in the range of an L-Lipschitz continuous ge... | [
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170 | Incremental Cross-View Mutual Distillation for Self-Supervised Medical CT Synthesis | [
"Chaowei Fang",
"Liang Wang",
"Dingwen Zhang",
"Jun Xu",
"Yixuan Yuan",
"Junwei Han"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Incremental_Cross-View_Mutual_Distillation_for_Self-Supervised_Medical_CT_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Fang_Incremental_Cross-View_Mutual_Distillation_for_Self-Supervised_Medical_CT_Synthesis_CVPR_2022_paper.pdf | null | 2112.10325 | cvf | @InProceedings{Fang_2022_CVPR,
author = {Fang, Chaowei and Wang, Liang and Zhang, Dingwen and Xu, Jun and Yuan, Yixuan and Han, Junwei},
title = {Incremental Cross-View Mutual Distillation for Self-Supervised Medical CT Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low within-slice resolution. Improving the inter-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper build... | [
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171 | Enhancing Adversarial Training With Second-Order Statistics of Weights | [
"Gaojie Jin",
"Xinping Yi",
"Wei Huang",
"Sven Schewe",
"Xiaowei Huang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Enhancing_Adversarial_Training_With_Second-Order_Statistics_of_Weights_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Enhancing_Adversarial_Training_With_Second-Order_Statistics_of_Weights_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jin_Enhancing_Adversarial_Training_CVPR_2022_supplemental.pdf | 2203.06020 | cvf | @InProceedings{Jin_2022_CVPR,
author = {Jin, Gaojie and Yi, Xinping and Huang, Wei and Schewe, Sven and Huang, Xiaowei},
title = {Enhancing Adversarial Training With Second-Order Statistics of Weights},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over model weights and adversarial perturbations, where the weights can be optimized through gradient descent methods like SGD. In this paper, we s... | [
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172 | Partially Does It: Towards Scene-Level FG-SBIR With Partial Input | [
"Pinaki Nath Chowdhury",
"Ayan Kumar Bhunia",
"Viswanatha Reddy Gajjala",
"Aneeshan Sain",
"Tao Xiang",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Chowdhury_Partially_Does_It_Towards_Scene-Level_FG-SBIR_With_Partial_Input_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chowdhury_Partially_Does_It_CVPR_2022_supplemental.pdf | 2203.14804 | cvf | @InProceedings{Chowdhury_2022_CVPR,
author = {Chowdhury, Pinaki Nath and Bhunia, Ayan Kumar and Gajjala, Viswanatha Reddy and Sain, Aneeshan and Xiang, Tao and Song, Yi-Zhe},
title = {Partially Does It: Towards Scene-Level FG-SBIR With Partial Input},
booktitle = {Proceedings of the IEEE/CVF Conferen... | We scrutinise an important observation plaguing scene-level sketch research -- that a significant portion of scene sketches are "partial". A quick pilot study reveals: (i) a scene sketch does not necessarily contain all objects in the corresponding photo, due to the subjective holistic interpretation of scenes, (ii) th... | [
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173 | Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo | [
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"Axi Niu",
"Zhinan Qiao",
"Chang D. Yoo",
"In So Kweon"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Dual_Temperature_Helps_Contrastive_Learning_Without_Many_Negative_Samples_Towards_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Dual_Temperature_Helps_Contrastive_Learning_Without_Many_Negative_Samples_Towards_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Dual_Temperature_Helps_CVPR_2022_supplemental.pdf | 2203.17248 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Chaoning and Zhang, Kang and Pham, Trung X. and Niu, Axi and Qiao, Zhinan and Yoo, Chang D. and Kweon, In So},
title = {Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo},
booktitle = ... | Contrastive learning (CL) is widely known to require many negative samples, 65536 in MoCo for instance, for which the performance of a dictionary-free framework is often inferior because the negative sample size (NSS) is limited by its mini-batch size (MBS). To decouple the NSS from the MBS, a dynamic dictionary has be... | [
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174 | Moving Window Regression: A Novel Approach to Ordinal Regression | [
"Nyeong-Ho Shin",
"Seon-Ho Lee",
"Chang-Su Kim"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Shin_Moving_Window_Regression_A_Novel_Approach_to_Ordinal_Regression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shin_Moving_Window_Regression_CVPR_2022_supplemental.zip | 2203.13122 | cvf | @InProceedings{Shin_2022_CVPR,
author = {Shin, Nyeong-Ho and Lee, Seon-Ho and Kim, Chang-Su},
title = {Moving Window Regression: A Novel Approach to Ordinal Regression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (rho-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors (rho-regressors) to pred... | [
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175 | UniCoRN: A Unified Conditional Image Repainting Network | [
"Jimeng Sun",
"Shuchen Weng",
"Zheng Chang",
"Si Li",
"Boxin Shi"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_UniCoRN_A_Unified_Conditional_Image_Repainting_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_UniCoRN_A_Unified_Conditional_Image_Repainting_Network_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Sun_2022_CVPR,
author = {Sun, Jimeng and Weng, Shuchen and Chang, Zheng and Li, Si and Shi, Boxin},
title = {UniCoRN: A Unified Conditional Image Repainting Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | Conditional image repainting (CIR) is an advanced image editing task, which requires the model to generate visual content in user-specified regions conditioned on multiple cross-modality constraints, and composite the visual content with the provided background seamlessly. Existing methods based on two-phase architectu... | [
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176 | Forecasting Characteristic 3D Poses of Human Actions | [
"Christian Diller",
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] | https://openaccess.thecvf.com/content/CVPR2022/html/Diller_Forecasting_Characteristic_3D_Poses_of_Human_Actions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Diller_Forecasting_Characteristic_3D_Poses_of_Human_Actions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Diller_Forecasting_Characteristic_3D_CVPR_2022_supplemental.pdf | 2011.15079 | cvf | @InProceedings{Diller_2022_CVPR,
author = {Diller, Christian and Funkhouser, Thomas and Dai, Angela},
title = {Forecasting Characteristic 3D Poses of Human Actions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | We propose the task of forecasting characteristic 3d poses: from a short sequence observation of a person, predict a future 3d pose of that person in a likely action-defining, characteristic pose - for instance, from observing a person picking up an apple, predict the pose of the person eating the apple. Prior work on ... | [
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177 | ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification | [
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"Yu Tian",
"Yuanhong Chen",
"Yuyuan Liu",
"Vasileios Belagiannis",
"Gustavo Carneiro"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_for_Semi-Supervised_Medical_Image_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_ACPL_Anti-Curriculum_Pseudo-Labelling_CVPR_2022_supplemental.pdf | 2111.12918 | cvf | @InProceedings{Liu_2022_CVPR,
author = {Liu, Fengbei and Tian, Yu and Chen, Yuanhong and Liu, Yuyuan and Belagiannis, Vasileios and Carneiro, Gustavo},
title = {ACPL: Anti-Curriculum Pseudo-Labelling for Semi-Supervised Medical Image Classification},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and multi-label (e.g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence).... | [
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178 | Learning to Deblur Using Light Field Generated and Real Defocus Images | [
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"Miuling Lam"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Ruan_Learning_to_Deblur_Using_Light_Field_Generated_and_Real_Defocus_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Ruan_Learning_to_Deblur_Using_Light_Field_Generated_and_Real_Defocus_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ruan_Learning_to_Deblur_CVPR_2022_supplemental.pdf | 2204.00367 | cvf | @InProceedings{Ruan_2022_CVPR,
author = {Ruan, Lingyan and Chen, Bin and Li, Jizhou and Lam, Miuling},
title = {Learning to Deblur Using Light Field Generated and Real Defocus Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur. While deep learning approach shows great promise in solving image restoration problems, defocus deblurring demands accurate training data that consists of all-in-focus and defocus image pairs, which is difficult to collect. Na... | [
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179 | Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection | [
"Nicolae-Cătălin Ristea",
"Neelu Madan",
"Radu Tudor Ionescu",
"Kamal Nasrollahi",
"Fahad Shahbaz Khan",
"Thomas B. Moeslund",
"Mubarak Shah"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Ristea_Self-Supervised_Predictive_Convolutional_Attentive_Block_for_Anomaly_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ristea_Self-Supervised_Predictive_Convolutional_CVPR_2022_supplemental.pdf | 2111.09099 | cvf | @InProceedings{Ristea_2022_CVPR,
author = {Ristea, Nicolae-C\u{a}t\u{a}lin and Madan, Neelu and Ionescu, Radu Tudor and Nasrollahi, Kamal and Khan, Fahad Shahbaz and Moeslund, Thomas B. and Shah, Mubarak},
title = {Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection},
bookt... | Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detection, a distinguished category of methods relies on predicting masked in... | [
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180 | Safe Self-Refinement for Transformer-Based Domain Adaptation | [
"Tao Sun",
"Cheng Lu",
"Tianshuo Zhang",
"Haibin Ling"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Safe_Self-Refinement_for_Transformer-Based_Domain_Adaptation_CVPR_2022_paper.pdf | null | 2204.07683 | cvf | @InProceedings{Sun_2022_CVPR,
author = {Sun, Tao and Lu, Cheng and Zhang, Tianshuo and Ling, Haibin},
title = {Safe Self-Refinement for Transformer-Based Domain Adaptation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source ___domain to solve tasks on a related unlabeled target ___domain. It is a challenging problem especially when a large ___domain gap lies between the source and target domains. In this paper we propose a novel solution named SSRT (Safe Self-Refinement for ... | [
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181 | Density-Preserving Deep Point Cloud Compression | [
"Yun He",
"Xinlin Ren",
"Danhang Tang",
"Yinda Zhang",
"Xiangyang Xue",
"Yanwei Fu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/He_Density-Preserving_Deep_Point_Cloud_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Density-Preserving_Deep_Point_Cloud_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Density-Preserving_Deep_Point_CVPR_2022_supplemental.pdf | 2204.12684 | cvf | @InProceedings{He_2022_CVPR,
author = {He, Yun and Ren, Xinlin and Tang, Danhang and Zhang, Yinda and Xue, Xiangyang and Fu, Yanwei},
title = {Density-Preserving Deep Point Cloud Compression},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Local density of point clouds is crucial for representing local details, but has been overlooked by existing point cloud compression methods. To address this, we propose a novel deep point cloud compression method that preserves local density information. Our method works in an auto-encoder fashion: the encoder downsam... | [
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182 | StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions | [
"Lukas Höllein",
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"Matthias Nießner"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Hollein_StyleMesh_Style_Transfer_for_Indoor_3D_Scene_Reconstructions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Hollein_StyleMesh_Style_Transfer_for_Indoor_3D_Scene_Reconstructions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hollein_StyleMesh_Style_Transfer_CVPR_2022_supplemental.pdf | 2112.01530 | title_snapshot | @InProceedings{Hollein_2022_CVPR,
author = {H\"ollein, Lukas and Johnson, Justin and Nie{\ss}ner, Matthias},
title = {StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | We apply style transfer on mesh reconstructions of indoor scenes. This enables VR applications like experiencing 3D environments painted in the style of a favorite artist. Style transfer typically operates on 2D images, making stylization of a mesh challenging. When optimized over a variety of poses, stylization patter... | [
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183 | Which Model To Transfer? Finding the Needle in the Growing Haystack | [
"Cedric Renggli",
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"Luka Rimanic",
"Joan Puigcerver",
"Carlos Riquelme",
"Ce Zhang",
"Mario Lučić"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Renggli_Which_Model_To_Transfer_Finding_the_Needle_in_the_Growing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Renggli_Which_Model_To_Transfer_Finding_the_Needle_in_the_Growing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Renggli_Which_Model_To_CVPR_2022_supplemental.pdf | 2010.06402 | cvf | @InProceedings{Renggli_2022_CVPR,
author = {Renggli, Cedric and Pinto, Andr\'e Susano and Rimanic, Luka and Puigcerver, Joan and Riquelme, Carlos and Zhang, Ce and Lu\v{c}i\'c, Mario},
title = {Which Model To Transfer? Finding the Needle in the Growing Haystack},
booktitle = {Proceedings of the IEEE/... | Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline. The emergence of rich model repositories, such as TensorFlow Hub, enables the practitioners and researchers to unleash t... | [
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184 | Fast and Unsupervised Action Boundary Detection for Action Segmentation | [
"Zexing Du",
"Xue Wang",
"Guoqing Zhou",
"Qing Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Du_2022_CVPR,
author = {Du, Zexing and Wang, Xue and Zhou, Guoqing and Wang, Qing},
title = {Fast and Unsupervised Action Boundary Detection for Action Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | To deal with the great number of untrimmed videos produced every day, we propose an efficient unsupervised action segmentation method by detecting boundaries, named action boundary detection (ABD). In particular, the proposed method has the following advantages: no training stage and low-latency inference. To detect ac... | [
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185 | Class-Incremental Learning With Strong Pre-Trained Models | [
"Tz-Ying Wu",
"Gurumurthy Swaminathan",
"Zhizhong Li",
"Avinash Ravichandran",
"Nuno Vasconcelos",
"Rahul Bhotika",
"Stefano Soatto"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Class-Incremental_Learning_With_Strong_Pre-Trained_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Class-Incremental_Learning_With_Strong_Pre-Trained_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Class-Incremental_Learning_With_CVPR_2022_supplemental.pdf | 2204.03634 | cvf | @InProceedings{Wu_2022_CVPR,
author = {Wu, Tz-Ying and Swaminathan, Gurumurthy and Li, Zhizhong and Ravichandran, Avinash and Vasconcelos, Nuno and Bhotika, Rahul and Soatto, Stefano},
title = {Class-Incremental Learning With Strong Pre-Trained Models},
booktitle = {Proceedings of the IEEE/CVF Confer... | Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. We hypothesize that a strong base model can p... | [
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186 | Robust Optimization As Data Augmentation for Large-Scale Graphs | [
"Kezhi Kong",
"Guohao Li",
"Mucong Ding",
"Zuxuan Wu",
"Chen Zhu",
"Bernard Ghanem",
"Gavin Taylor",
"Tom Goldstein"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Kong_Robust_Optimization_As_Data_Augmentation_for_Large-Scale_Graphs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kong_Robust_Optimization_As_CVPR_2022_supplemental.pdf | 2010.09891 | cvf | @InProceedings{Kong_2022_CVPR,
author = {Kong, Kezhi and Li, Guohao and Ding, Mucong and Wu, Zuxuan and Zhu, Chen and Ghanem, Bernard and Taylor, Gavin and Goldstein, Tom},
title = {Robust Optimization As Data Augmentation for Large-Scale Graphs},
booktitle = {Proceedings of the IEEE/CVF Conference o... | Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks). While most existing graph regularizers focus on manipulating graph topological structures by adding/r... | [
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187 | Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks With Implicit Gradients | [
"Kaidong Li",
"Ziming Zhang",
"Cuncong Zhong",
"Guanghui Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Robust_Structured_Declarative_Classifiers_for_3D_Point_Clouds_Defending_Adversarial_CVPR_2022_paper.pdf | null | 2203.15245 | cvf | @InProceedings{Li_2022_CVPR,
author = {Li, Kaidong and Zhang, Ziming and Zhong, Cuncong and Wang, Guanghui},
title = {Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks With Implicit Gradients},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer ... | Deep neural networks for 3D point cloud classification, such as PointNet, have been demonstrated to be vulnerable to adversarial attacks. Current adversarial defenders often learn to denoise the (attacked) point clouds by reconstruction, and then feed them to the classifiers as input. In contrast to the literature, we ... | [
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188 | PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes | [
"Yu-Ying Yeh",
"Zhengqin Li",
"Yannick Hold-Geoffroy",
"Rui Zhu",
"Zexiang Xu",
"Miloš Hašan",
"Kalyan Sunkavalli",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yeh_PhotoScene_Photorealistic_Material_and_Lighting_Transfer_for_Indoor_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yeh_PhotoScene_Photorealistic_Material_CVPR_2022_supplemental.zip | 2207.00757 | title_snapshot | @InProceedings{Yeh_2022_CVPR,
author = {Yeh, Yu-Ying and Li, Zhengqin and Hold-Geoffroy, Yannick and Zhu, Rui and Xu, Zexiang and Ha\v{s}an, Milo\v{s} and Sunkavalli, Kalyan and Chandraker, Manmohan},
title = {PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes},
booktitle = {... | Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a phot... | [
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189 | Improving the Transferability of Targeted Adversarial Examples Through Object-Based Diverse Input | [
"Junyoung Byun",
"Seungju Cho",
"Myung-Joon Kwon",
"Hee-Seon Kim",
"Changick Kim"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Byun_Improving_the_Transferability_of_Targeted_Adversarial_Examples_Through_Object-Based_Diverse_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Byun_Improving_the_Transferability_of_Targeted_Adversarial_Examples_Through_Object-Based_Diverse_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Byun_Improving_the_Transferability_CVPR_2022_supplemental.pdf | 2203.09123 | cvf | @InProceedings{Byun_2022_CVPR,
author = {Byun, Junyoung and Cho, Seungju and Kwon, Myung-Joon and Kim, Hee-Seon and Kim, Changick},
title = {Improving the Transferability of Targeted Adversarial Examples Through Object-Based Diverse Input},
booktitle = {Proceedings of the IEEE/CVF Conference on Compu... | The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical applicability. To maximize the transfer success rate, adversarial examples should avoid overfitting to the source model, and image augmentatio... | [
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190 | IRON: Inverse Rendering by Optimizing Neural SDFs and Materials From Photometric Images | [
"Kai Zhang",
"Fujun Luan",
"Zhengqi Li",
"Noah Snavely"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_IRON_Inverse_Rendering_by_Optimizing_Neural_SDFs_and_Materials_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_IRON_Inverse_Rendering_by_Optimizing_Neural_SDFs_and_Materials_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_IRON_Inverse_Rendering_CVPR_2022_supplemental.pdf | 2204.02232 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Kai and Luan, Fujun and Li, Zhengqi and Snavely, Noah},
title = {IRON: Inverse Rendering by Optimizing Neural SDFs and Materials From Photometric Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines. We propose a neural inverse rendering pipeline called IRON that operates on photome... | [
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191 | ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer | [
"Ruohan Gao",
"Zilin Si",
"Yen-Yu Chang",
"Samuel Clarke",
"Jeannette Bohg",
"Li Fei-Fei",
"Wenzhen Yuan",
"Jiajun Wu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_ObjectFolder_2.0_A_Multisensory_Object_Dataset_for_Sim2Real_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_ObjectFolder_2.0_A_Multisensory_Object_Dataset_for_Sim2Real_Transfer_CVPR_2022_paper.pdf | null | 2204.02389 | title_snapshot | @InProceedings{Gao_2022_CVPR,
author = {Gao, Ruohan and Si, Zilin and Chang, Yen-Yu and Clarke, Samuel and Bohg, Jeannette and Fei-Fei, Li and Yuan, Wenzhen and Wu, Jiajun},
title = {ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfer},
booktitle = {Proceedings of the IEEE/CVF Confe... | Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. ObjectFolder 1.0 is a recent dataset that introduces 100 virtualized objects with visual, auditory, and tactile sensory data.... | [
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192 | Versatile Multi-Modal Pre-Training for Human-Centric Perception | [
"Fangzhou Hong",
"Liang Pan",
"Zhongang Cai",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Versatile_Multi-Modal_Pre-Training_for_Human-Centric_Perception_CVPR_2022_paper.pdf | null | 2203.13815 | cvf | @InProceedings{Hong_2022_CVPR,
author = {Hong, Fangzhou and Pan, Liang and Cai, Zhongang and Liu, Ziwei},
title = {Versatile Multi-Modal Pre-Training for Human-Centric Perception},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | Human-centric perception plays a vital role in vision and graphics. But their data annotations are prohibitively expensive. Therefore, it is desirable to have a versatile pre-train model that serves as a foundation for data-efficient downstream tasks transfer. To this end, we propose the Human-Centric Multi-Modal Contr... | [
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193 | 360MonoDepth: High-Resolution 360deg Monocular Depth Estimation | [
"Manuel Rey-Area",
"Mingze Yuan",
"Christian Richardt"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Rey-Area_360MonoDepth_High-Resolution_360deg_Monocular_Depth_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Rey-Area_360MonoDepth_High-Resolution_360deg_Monocular_Depth_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rey-Area_360MonoDepth_High-Resolution_360deg_CVPR_2022_supplemental.pdf | 2111.15669 | title_judge | @InProceedings{Rey-Area_2022_CVPR,
author = {Rey-Area, Manuel and Yuan, Mingze and Richardt, Christian},
title = {360MonoDepth: High-Resolution 360deg Monocular Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | 360deg cameras can capture complete environments in a single shot, which makes 360deg imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360deg data, particularly for high resolutions like 2K (2048x1024) and beyond that are important for novel-view synthesis and ... | [
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194 | Splicing ViT Features for Semantic Appearance Transfer | [
"Narek Tumanyan",
"Omer Bar-Tal",
"Shai Bagon",
"Tali Dekel"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Tumanyan_Splicing_ViT_Features_for_Semantic_Appearance_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Tumanyan_Splicing_ViT_Features_for_Semantic_Appearance_Transfer_CVPR_2022_paper.pdf | null | 2201.00424 | title_snapshot | @InProceedings{Tumanyan_2022_CVPR,
author = {Tumanyan, Narek and Bar-Tal, Omer and Bagon, Shai and Dekel, Tali},
title = {Splicing ViT Features for Semantic Appearance Transfer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their semantically related objects in a target appearance image. Our method wor... | [
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195 | Contrastive Regression for Domain Adaptation on Gaze Estimation | [
"Yaoming Wang",
"Yangzhou Jiang",
"Jin Li",
"Bingbing Ni",
"Wenrui Dai",
"Chenglin Li",
"Hongkai Xiong",
"Teng Li"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Contrastive_Regression_for_Domain_Adaptation_on_Gaze_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Contrastive_Regression_for_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Wang_2022_CVPR,
author = {Wang, Yaoming and Jiang, Yangzhou and Li, Jin and Ni, Bingbing and Dai, Wenrui and Li, Chenglin and Xiong, Hongkai and Li, Teng},
title = {Contrastive Regression for Domain Adaptation on Gaze Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | Appearance-based Gaze Estimation leverages deep neural networks to regress the gaze direction from monocular images and achieve impressive performance. However, its success depends on expensive and cumbersome annotation capture. When lacking precise annotation, the large ___domain gap hinders the performance of trained mo... | [
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196 | MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction | [
"Mihee Lee",
"Samuel S. Sohn",
"Seonghyeon Moon",
"Sejong Yoon",
"Mubbasir Kapadia",
"Vladimir Pavlovic"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_MUSE-VAE_Multi-Scale_VAE_for_Environment-Aware_Long_Term_Trajectory_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_MUSE-VAE_Multi-Scale_VAE_CVPR_2022_supplemental.pdf | 2201.07189 | title_snapshot | @InProceedings{Lee_2022_CVPR,
author = {Lee, Mihee and Sohn, Samuel S. and Moon, Seonghyeon and Yoon, Sejong and Kapadia, Mubbasir and Pavlovic, Vladimir},
title = {MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Accurate long-term trajectory prediction in complex scenes, where multiple agents (e.g., pedestrians or vehicles) interact with each other and the environment while attempting to accomplish diverse and often unknown goals, is a challenging stochastic forecasting problem. In this work, we propose MUSE-VAE, a new probabi... | [
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197 | Multi-View Consistent Generative Adversarial Networks for 3D-Aware Image Synthesis | [
"Xuanmeng Zhang",
"Zhedong Zheng",
"Daiheng Gao",
"Bang Zhang",
"Pan Pan",
"Yi Yang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Multi-View_Consistent_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Multi-View_Consistent_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Multi-View_Consistent_Generative_CVPR_2022_supplemental.pdf | 2204.06307 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Xuanmeng and Zheng, Zhedong and Gao, Daiheng and Zhang, Bang and Pan, Pan and Yang, Yi},
title = {Multi-View Consistent Generative Adversarial Networks for 3D-Aware Image Synthesis},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | 3D-aware image synthesis aims to generate images of objects from multiple views by learning a 3D representation. However, one key challenge remains: existing approaches lack geometry constraints, hence usually fail to generate multi-view consistent images. To address this challenge, we propose Multi-View Consistent Gen... | [
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198 | Putting People in Their Place: Monocular Regression of 3D People in Depth | [
"Yu Sun",
"Wu Liu",
"Qian Bao",
"Yili Fu",
"Tao Mei",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Putting_People_in_Their_Place_Monocular_Regression_of_3D_People_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Putting_People_in_CVPR_2022_supplemental.pdf | 2112.08274 | cvf | @InProceedings{Sun_2022_CVPR,
author = {Sun, Yu and Liu, Wu and Bao, Qian and Fu, Yili and Mei, Tao and Black, Michael J.},
title = {Putting People in Their Place: Monocular Regression of 3D People in Depth},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Given an image with multiple people, our goal is to directly regress the pose and shape of all the people as well as their relative depth. Inferring the depth of a person in an image, however, is fundamentally ambiguous without knowing their height. This is particularly problematic when the scene contains people of ver... | [
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199 | POCO: Point Convolution for Surface Reconstruction | [
"Alexandre Boulch",
"Renaud Marlet"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Boulch_POCO_Point_Convolution_for_Surface_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Boulch_POCO_Point_Convolution_for_Surface_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Boulch_POCO_Point_Convolution_CVPR_2022_supplemental.pdf | 2201.01831 | cvf | @InProceedings{Boulch_2022_CVPR,
author = {Boulch, Alexandre and Marlet, Renaud},
title = {POCO: Point Convolution for Surface Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
p... | Implicit neural networks have been successfully used for surface reconstruction from point clouds. However, many of them face scalability issues as they encode the isosurface function of a whole object or scene into a single latent vector. To overcome this limitation, a few approaches infer latent vectors on a coarse r... | [
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