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0 | Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification | [
"Haowei Zhu",
"Wenjing Ke",
"Dong Li",
"Ji Liu",
"Lu Tian",
"Yi Shan"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Dual_Cross-Attention_Learning_for_Fine-Grained_Visual_Categorization_and_Object_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Dual_Cross-Attention_Learning_CVPR_2022_supplemental.pdf | 2205.02151 | cvf | @InProceedings{Zhu_2022_CVPR,
author = {Zhu, Haowei and Ke, Wenjing and Li, Dong and Liu, Ji and Tian, Lu and Shan, Yi},
title = {Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore how to extend self-attention modules to better learn subtle feature embeddings for recognizing fine-grained objects... | [
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1 | SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization | [
"Canjie Luo",
"Lianwen Jin",
"Jingdong Chen"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_SimAN_Exploring_Self-Supervised_Representation_Learning_of_Scene_Text_via_Similarity-Aware_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_SimAN_Exploring_Self-Supervised_Representation_Learning_of_Scene_Text_via_Similarity-Aware_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luo_SimAN_Exploring_Self-Supervised_CVPR_2022_supplemental.pdf | 2203.10492 | cvf | @InProceedings{Luo_2022_CVPR,
author = {Luo, Canjie and Jin, Lianwen and Chen, Jingdong},
title = {SimAN: Exploring Self-Supervised Representation Learning of Scene Text via Similarity-Aware Normalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | Recently self-supervised representation learning has drawn considerable attention from the scene text recognition community. Different from previous studies using contrastive learning, we tackle the issue from an alternative perspective, i.e., by formulating the representation learning scheme in a generative manner. Ty... | [
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2 | GASP, a Generalized Framework for Agglomerative Clustering of Signed Graphs and Its Application to Instance Segmentation | [
"Alberto Bailoni",
"Constantin Pape",
"Nathan Hütsch",
"Steffen Wolf",
"Thorsten Beier",
"Anna Kreshuk",
"Fred A. Hamprecht"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Bailoni_GASP_a_Generalized_Framework_for_Agglomerative_Clustering_of_Signed_Graphs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Bailoni_GASP_a_Generalized_Framework_for_Agglomerative_Clustering_of_Signed_Graphs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bailoni_GASP_a_Generalized_CVPR_2022_supplemental.pdf | 1906.11713 | title_snapshot | @InProceedings{Bailoni_2022_CVPR,
author = {Bailoni, Alberto and Pape, Constantin and H\"utsch, Nathan and Wolf, Steffen and Beier, Thorsten and Kreshuk, Anna and Hamprecht, Fred A.},
title = {GASP, a Generalized Framework for Agglomerative Clustering of Signed Graphs and Its Application to Instance Segm... | We propose a theoretical framework that generalizes simple and fast algorithms for hierarchical agglomerative clustering to weighted graphs with both attractive and repulsive interactions between the nodes. This framework defines GASP, a Generalized Algorithm for Signed graph Partitioning, and allows us to explore many... | [
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3 | Estimating Example Difficulty Using Variance of Gradients | [
"Chirag Agarwal",
"Daniel D'souza",
"Sara Hooker"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Agarwal_Estimating_Example_Difficulty_Using_Variance_of_Gradients_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Agarwal_Estimating_Example_Difficulty_Using_Variance_of_Gradients_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Agarwal_Estimating_Example_Difficulty_CVPR_2022_supplemental.pdf | 2008.11600 | cvf | @InProceedings{Agarwal_2022_CVPR,
author = {Agarwal, Chirag and D'souza, Daniel and Hooker, Sara},
title = {Estimating Example Difficulty Using Variance of Gradients},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | In machine learning, a question of great interest is understanding what examples are challenging for a model to classify. Identifying atypical examples ensures the safe deployment of models, isolates samples that require further human inspection, and provides interpretability into model behavior. In this work, we propo... | [
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4 | One Loss for Quantization: Deep Hashing With Discrete Wasserstein Distributional Matching | [
"Khoa D. Doan",
"Peng Yang",
"Ping Li"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Doan_One_Loss_for_Quantization_Deep_Hashing_With_Discrete_Wasserstein_Distributional_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Doan_One_Loss_for_Quantization_Deep_Hashing_With_Discrete_Wasserstein_Distributional_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Doan_One_Loss_for_CVPR_2022_supplemental.pdf | 2205.15721 | cvf | @InProceedings{Doan_2022_CVPR,
author = {Doan, Khoa D. and Yang, Peng and Li, Ping},
title = {One Loss for Quantization: Deep Hashing With Discrete Wasserstein Distributional Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Image hashing is a principled approximate nearest neighbor approach to find similar items to a query in a large collection of images. Hashing aims to learn a binary-output function that maps an image to a binary vector. For optimal retrieval performance, producing balanced hash codes with low-quantization error to brid... | [
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5 | Pixel Screening Based Intermediate Correction for Blind Deblurring | [
"Meina Zhang",
"Yingying Fang",
"Guoxi Ni",
"Tieyong Zeng"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Pixel_Screening_Based_Intermediate_Correction_for_Blind_Deblurring_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Pixel_Screening_Based_Intermediate_Correction_for_Blind_Deblurring_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Pixel_Screening_Based_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Meina and Fang, Yingying and Ni, Guoxi and Zeng, Tieyong},
title = {Pixel Screening Based Intermediate Correction for Blind Deblurring},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Blind deblurring has attracted much interest with its wide applications in reality. The blind deblurring problem is usually solved by estimating the intermediate kernel and the intermediate image alternatively, which will finally converge to the blurring kernel of the observed image. Numerous works have been proposed t... | [
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6 | Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast | [
"Ye Du",
"Zehua Fu",
"Qingjie Liu",
"Yunhong Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Weakly_Supervised_Semantic_Segmentation_by_Pixel-to-Prototype_Contrast_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_Weakly_Supervised_Semantic_Segmentation_by_Pixel-to-Prototype_Contrast_CVPR_2022_paper.pdf | null | 2110.07110 | cvf | @InProceedings{Du_2022_CVPR,
author = {Du, Ye and Fu, Zehua and Liu, Qingjie and Wang, Yunhong},
title = {Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {J... | Though image-level weakly supervised semantic segmentation (WSSS) has achieved great progress with Class Activation Maps (CAMs) as the cornerstone, the large supervision gap between classification and segmentation still hampers the model to generate more complete and precise pseudo masks for segmentation. In this study... | [
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7 | Controllable Animation of Fluid Elements in Still Images | [
"Aniruddha Mahapatra",
"Kuldeep Kulkarni"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Mahapatra_Controllable_Animation_of_Fluid_Elements_in_Still_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Mahapatra_Controllable_Animation_of_Fluid_Elements_in_Still_Images_CVPR_2022_paper.pdf | null | 2112.03051 | cvf | @InProceedings{Mahapatra_2022_CVPR,
author = {Mahapatra, Aniruddha and Kulkarni, Kuldeep},
title = {Controllable Animation of Fluid Elements in Still Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | We propose a method to interactively control the animation of fluid elements in still images to generate cinemagraphs. Specifically, we focus on the animation of fluid elements like water, smoke, fire, which have the properties of repeating textures and continuous fluid motion. Taking inspiration from prior works, we r... | [
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8 | Holocurtains: Programming Light Curtains via Binary Holography | [
"Dorian Chan",
"Srinivasa G. Narasimhan",
"Matthew O'Toole"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Holocurtains_Programming_Light_Curtains_via_Binary_Holography_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_Holocurtains_Programming_Light_Curtains_via_Binary_Holography_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chan_Holocurtains_Programming_Light_CVPR_2022_supplemental.zip | null | null | @InProceedings{Chan_2022_CVPR,
author = {Chan, Dorian and Narasimhan, Srinivasa G. and O'Toole, Matthew},
title = {Holocurtains: Programming Light Curtains via Binary Holography},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Light curtain systems are designed for detecting the presence of objects within a user-defined 3D region of space, which has many applications across vision and robotics. However, the shape of light curtains have so far been limited to ruled surfaces, i.e., surfaces composed of straight lines. In this work, we propose ... | [
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9 | Recurrent Dynamic Embedding for Video Object Segmentation | [
"Mingxing Li",
"Li Hu",
"Zhiwei Xiong",
"Bang Zhang",
"Pan Pan",
"Dong Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Recurrent_Dynamic_Embedding_for_Video_Object_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Recurrent_Dynamic_Embedding_for_Video_Object_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Recurrent_Dynamic_Embedding_CVPR_2022_supplemental.pdf | 2205.03761 | cvf | @InProceedings{Li_2022_CVPR,
author = {Li, Mingxing and Hu, Li and Xiong, Zhiwei and Zhang, Bang and Pan, Pan and Liu, Dong},
title = {Recurrent Dynamic Embedding for Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Space-time memory (STM) based video object segmentation (VOS) networks usually keep increasing memory bank every several frames, which shows excellent performance. However, 1) the hardware cannot withstand the ever-increasing memory requirements as the video length increases. 2) Storing lots of information inevitably i... | [
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10 | Deep Hierarchical Semantic Segmentation | [
"Liulei Li",
"Tianfei Zhou",
"Wenguan Wang",
"Jianwu Li",
"Yi Yang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Deep_Hierarchical_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Deep_Hierarchical_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Deep_Hierarchical_Semantic_CVPR_2022_supplemental.pdf | 2203.14335 | cvf | @InProceedings{Li_2022_CVPR,
author = {Li, Liulei and Zhou, Tianfei and Wang, Wenguan and Li, Jianwu and Yang, Yi},
title = {Deep Hierarchical Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
ye... | Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world in multiple levels. However, such hierarchical reasoning ability of human perception remains largely unexplored in current literature of semantic segmentation. Exist... | [
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11 | f-SfT: Shape-From-Template With a Physics-Based Deformation Model | [
"Navami Kairanda",
"Edith Tretschk",
"Mohamed Elgharib",
"Christian Theobalt",
"Vladislav Golyanik"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Kairanda_f-SfT_Shape-From-Template_With_a_Physics-Based_Deformation_Model_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Kairanda_f-SfT_Shape-From-Template_With_a_Physics-Based_Deformation_Model_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kairanda_f-SfT_Shape-From-Template_With_CVPR_2022_supplemental.pdf | 2203.11938 | title_judge | @InProceedings{Kairanda_2022_CVPR,
author = {Kairanda, Navami and Tretschk, Edith and Elgharib, Mohamed and Theobalt, Christian and Golyanik, Vladislav},
title = {f-SfT: Shape-From-Template With a Physics-Based Deformation Model},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Shape-from-Template (SfT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric an... | [
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12 | Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism | [
"Binbin Yang",
"Xinchi Deng",
"Han Shi",
"Changlin Li",
"Gengwei Zhang",
"Hang Xu",
"Shen Zhao",
"Liang Lin",
"Xiaodan Liang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Continual_Object_Detection_via_Prototypical_Task_Correlation_Guided_Gating_Mechanism_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Continual_Object_Detection_via_Prototypical_Task_Correlation_Guided_Gating_Mechanism_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Continual_Object_Detection_CVPR_2022_supplemental.pdf | 2205.03055 | cvf | @InProceedings{Yang_2022_CVPR,
author = {Yang, Binbin and Deng, Xinchi and Shi, Han and Li, Changlin and Zhang, Gengwei and Xu, Hang and Zhao, Shen and Lin, Liang and Liang, Xiaodan},
title = {Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism},
booktitle = {Proceedi... | Continual learning is a challenging real-world problem for constructing a mature AI system when data are provided in a streaming fashion. Despite recent progress in continual classification, the researches of continual object detection are impeded by the diverse sizes and numbers of objects in each image. Different fro... | [
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13 | DATA: Domain-Aware and Task-Aware Self-Supervised Learning | [
"Qing Chang",
"Junran Peng",
"Lingxi Xie",
"Jiajun Sun",
"Haoran Yin",
"Qi Tian",
"Zhaoxiang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_DATA_Domain-Aware_and_Task-Aware_Self-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Chang_DATA_Domain-Aware_and_Task-Aware_Self-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chang_DATA_Domain-Aware_and_CVPR_2022_supplemental.zip | 2203.09041 | cvf | @InProceedings{Chang_2022_CVPR,
author = {Chang, Qing and Peng, Junran and Xie, Lingxi and Sun, Jiajun and Yin, Haoran and Tian, Qi and Zhang, Zhaoxiang},
title = {DATA: Domain-Aware and Task-Aware Self-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | The paradigm of training models on massive data without label through self-supervised learning (SSL) and finetuning on many downstream tasks has become a trend recently. However, due to the high training costs and the unconsciousness of downstream usages, most self-supervised learning methods lack the capability to cor... | [
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14 | TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation | [
"Ruihang Chu",
"Xiaoqing Ye",
"Zhengzhe Liu",
"Xiao Tan",
"Xiaojuan Qi",
"Chi-Wing Fu",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Chu_TWIST_Two-Way_Inter-Label_Self-Training_for_Semi-Supervised_3D_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Chu_TWIST_Two-Way_Inter-Label_Self-Training_for_Semi-Supervised_3D_Instance_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chu_TWIST_Two-Way_Inter-Label_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Chu_2022_CVPR,
author = {Chu, Ruihang and Ye, Xiaoqing and Liu, Zhengzhe and Tan, Xiao and Qi, Xiaojuan and Fu, Chi-Wing and Jia, Jiaya},
title = {TWIST: Two-Way Inter-Label Self-Training for Semi-Supervised 3D Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference... | We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation. To leverage the unlabeled data to boost model performance, we present a novel Two-Way Inter-label Self-Training framework named TWIST. It exploits inherent correlations between semantic understanding and ... | [
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15 | Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection From Point Clouds | [
"Chenhang He",
"Ruihuang Li",
"Shuai Li",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/He_Voxel_Set_Transformer_A_Set-to-Set_Approach_to_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Voxel_Set_Transformer_A_Set-to-Set_Approach_to_3D_Object_Detection_CVPR_2022_paper.pdf | null | 2203.10314 | cvf | @InProceedings{He_2022_CVPR,
author = {He, Chenhang and Li, Ruihuang and Li, Shuai and Zhang, Lei},
title = {Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection From Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to apply the self-attention underlying transformer on large-scale point cloud data because point cloud is a long sequence and unevenly distributed in 3D space. To solve this issue, existing methods usually compute self... | [
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16 | Learning Adaptive Warping for Real-World Rolling Shutter Correction | [
"Mingdeng Cao",
"Zhihang Zhong",
"Jiahao Wang",
"Yinqiang Zheng",
"Yujiu Yang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_Learning_Adaptive_Warping_for_Real-World_Rolling_Shutter_Correction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_Learning_Adaptive_Warping_for_Real-World_Rolling_Shutter_Correction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_Learning_Adaptive_Warping_CVPR_2022_supplemental.pdf | 2204.13886 | cvf | @InProceedings{Cao_2022_CVPR,
author = {Cao, Mingdeng and Zhong, Zhihang and Wang, Jiahao and Zheng, Yinqiang and Yang, Yujiu},
title = {Learning Adaptive Warping for Real-World Rolling Shutter Correction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | This paper proposes a real-world rolling shutter (RS) correction dataset, BS-RSC, and a corresponding model to correct the RS frames in a distorted video. Mobile devices in the consumer market with CMOS-based sensors for video capture often result in rolling shutter effects when relative movements occur during the vide... | [
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17 | Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning | [
"Xiangyu Li",
"Xu Yang",
"Kun Wei",
"Cheng Deng",
"Muli Yang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Siamese_Contrastive_Embedding_Network_for_Compositional_Zero-Shot_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Siamese_Contrastive_Embedding_Network_for_Compositional_Zero-Shot_Learning_CVPR_2022_paper.pdf | null | 2206.14475 | title_snapshot | @InProceedings{Li_2022_CVPR,
author = {Li, Xiangyu and Yang, Xu and Wei, Kun and Deng, Cheng and Yang, Muli},
title = {Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual appearance while entangled with different objects, CZSL is still a challenging task. Some methods recognize state and object with two trained... | [
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18 | Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions | [
"Huaizu Jiang",
"Xiaojian Ma",
"Weili Nie",
"Zhiding Yu",
"Yuke Zhu",
"Anima Anandkumar"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Bongard-HOI_Benchmarking_Few-Shot_Visual_Reasoning_for_Human-Object_Interactions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Bongard-HOI_Benchmarking_Few-Shot_CVPR_2022_supplemental.pdf | 2205.13803 | title_snapshot | @InProceedings{Jiang_2022_CVPR,
author = {Jiang, Huaizu and Ma, Xiaojian and Nie, Weili and Yu, Zhiding and Zhu, Yuke and Anandkumar, Anima},
title = {Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vi... | A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts. We introduce Bongard-HOI, a new visual reasoning benchmark that focuses on compositional learning of human-object inte... | [
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19 | RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures | [
"Chengjie Niu",
"Manyi Li",
"Kai Xu",
"Hao Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Niu_RIM-Net_Recursive_Implicit_Fields_for_Unsupervised_Learning_of_Hierarchical_Shape_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Niu_RIM-Net_Recursive_Implicit_Fields_for_Unsupervised_Learning_of_Hierarchical_Shape_CVPR_2022_paper.pdf | null | 2201.12763 | title_snapshot | @InProceedings{Niu_2022_CVPR,
author = {Niu, Chengjie and Li, Manyi and Xu, Kai and Zhang, Hao},
title = {RIM-Net: Recursive Implicit Fields for Unsupervised Learning of Hierarchical Shape Structures},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | We introduce RIM-Net, a neural network which learns recursive implicit fields for unsupervised inference of hierarchical shape structures. Our network recursively decomposes an input 3D shape into two parts, resulting in a binary tree hierarchy. Each level of the tree corresponds to an assembly of shape parts, represen... | [
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20 | Do Learned Representations Respect Causal Relationships? | [
"Lan Wang",
"Vishnu Naresh Boddeti"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Do_Learned_Representations_Respect_Causal_Relationships_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Do_Learned_Representations_Respect_Causal_Relationships_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Do_Learned_Representations_CVPR_2022_supplemental.pdf | 2204.00762 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Lan and Boddeti, Vishnu Naresh},
title = {Do Learned Representations Respect Causal Relationships?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
... | Data often has many semantic attributes that are causally associated with each other. But do attribute-specific learned representations of data also respect the same causal relations? We answer this question in three steps. First, we introduce NCINet, an approach for observational causal discovery from high-dimensional... | [
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21 | ZebraPose: Coarse To Fine Surface Encoding for 6DoF Object Pose Estimation | [
"Yongzhi Su",
"Mahdi Saleh",
"Torben Fetzer",
"Jason Rambach",
"Nassir Navab",
"Benjamin Busam",
"Didier Stricker",
"Federico Tombari"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Su_ZebraPose_Coarse_To_Fine_Surface_Encoding_for_6DoF_Object_Pose_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Su_ZebraPose_Coarse_To_CVPR_2022_supplemental.pdf | 2203.09418 | cvf | @InProceedings{Su_2022_CVPR,
author = {Su, Yongzhi and Saleh, Mahdi and Fetzer, Torben and Rambach, Jason and Navab, Nassir and Busam, Benjamin and Stricker, Didier and Tombari, Federico},
title = {ZebraPose: Coarse To Fine Surface Encoding for 6DoF Object Pose Estimation},
booktitle = {Proceedings o... | Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improveme... | [
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22 | ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic | [
"Yoad Tewel",
"Yoav Shalev",
"Idan Schwartz",
"Lior Wolf"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Tewel_ZeroCap_Zero-Shot_Image-to-Text_Generation_for_Visual-Semantic_Arithmetic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Tewel_ZeroCap_Zero-Shot_Image-to-Text_Generation_for_Visual-Semantic_Arithmetic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tewel_ZeroCap_Zero-Shot_Image-to-Text_CVPR_2022_supplemental.pdf | 2111.14447 | cvf | @InProceedings{Tewel_2022_CVPR,
author = {Tewel, Yoad and Shalev, Yoav and Schwartz, Idan and Wolf, Lior},
title = {ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. While such models can provide a powerful score for matching and subsequent zero-shot tasks, they are not capable of generating caption given an image. In this work, we repurpose such models to ge... | [
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23 | Learning To Affiliate: Mutual Centralized Learning for Few-Shot Classification | [
"Yang Liu",
"Weifeng Zhang",
"Chao Xiang",
"Tu Zheng",
"Deng Cai",
"Xiaofei He"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_To_Affiliate_Mutual_Centralized_Learning_for_Few-Shot_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_To_Affiliate_Mutual_Centralized_Learning_for_Few-Shot_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Learning_To_Affiliate_CVPR_2022_supplemental.pdf | 2106.05517 | cvf | @InProceedings{Liu_2022_CVPR,
author = {Liu, Yang and Zhang, Weifeng and Xiang, Chao and Zheng, Tu and Cai, Deng and He, Xiaofei},
title = {Learning To Affiliate: Mutual Centralized Learning for Few-Shot Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to accommodate new tasks, given only a few examples. To handle the limited-data in few-shot regimes, recent methods tend to collectively use a set of local features to densely represent an image instead of using a mixed global feature. They g... | [
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24 | CAPRI-Net: Learning Compact CAD Shapes With Adaptive Primitive Assembly | [
"Fenggen Yu",
"Zhiqin Chen",
"Manyi Li",
"Aditya Sanghi",
"Hooman Shayani",
"Ali Mahdavi-Amiri",
"Hao Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_CAPRI-Net_Learning_Compact_CAD_Shapes_With_Adaptive_Primitive_Assembly_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_CAPRI-Net_Learning_Compact_CAD_Shapes_With_Adaptive_Primitive_Assembly_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_CAPRI-Net_Learning_Compact_CVPR_2022_supplemental.pdf | 2104.05652 | title_snapshot | @InProceedings{Yu_2022_CVPR,
author = {Yu, Fenggen and Chen, Zhiqin and Li, Manyi and Sanghi, Aditya and Shayani, Hooman and Mahdavi-Amiri, Ali and Zhang, Hao},
title = {CAPRI-Net: Learning Compact CAD Shapes With Adaptive Primitive Assembly},
booktitle = {Proceedings of the IEEE/CVF Conference on Co... | We introduce CAPRI-Net, a self-supervised neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies. Given an input 3D shape, our network reconstructs it by an assembly of quadric surface primitives via construct... | [
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25 | ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework | [
"Chunnan Wang",
"Xiang Chen",
"Junzhe Wang",
"Hongzhi Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Wang_2022_CVPR,
author = {Wang, Chunnan and Chen, Xiang and Wang, Junzhe and Wang, Hongzhi},
title = {ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Although the Trajectory Prediction (TP) model has achieved great success in computer vision and robotics fields, its architecture and training scheme design rely on heavy manual work and ___domain knowledge, which is not friendly to common users. Besides, the existing works ignore Federated Learning (FL) scenarios, failin... | [
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26 | Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning | [
"Moslem Yazdanpanah",
"Aamer Abdul Rahman",
"Muawiz Chaudhary",
"Christian Desrosiers",
"Mohammad Havaei",
"Eugene Belilovsky",
"Samira Ebrahimi Kahou"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Yazdanpanah_2022_CVPR,
author = {Yazdanpanah, Moslem and Rahman, Aamer Abdul and Chaudhary, Muawiz and Desrosiers, Christian and Havaei, Mohammad and Belilovsky, Eugene and Kahou, Samira Ebrahimi},
title = {Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning},
boo... | Batch Normalization is a staple of computer vision models, including those employed in few-shot learning. Batch Normalization layers in convolutional neural networks are composed of a normalization step, followed by a shift and scale of these normalized features applied via the per-channel trainable affine parameters g... | [
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27 | Bridging the Gap Between Classification and Localization for Weakly Supervised Object Localization | [
"Eunji Kim",
"Siwon Kim",
"Jungbeom Lee",
"Hyunwoo Kim",
"Sungroh Yoon"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Bridging_the_Gap_Between_Classification_and_Localization_for_Weakly_Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Bridging_the_Gap_Between_Classification_and_Localization_for_Weakly_Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Bridging_the_Gap_CVPR_2022_supplemental.pdf | 2204.00220 | cvf | @InProceedings{Kim_2022_CVPR,
author = {Kim, Eunji and Kim, Siwon and Lee, Jungbeom and Kim, Hyunwoo and Yoon, Sungroh},
title = {Bridging the Gap Between Classification and Localization for Weakly Supervised Object Localization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision... | Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels. Most existing methods use a class activation map (CAM) to generate a localization map; however, a CAM identifies only the most discriminative parts of a target object rather... | [
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28 | Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation | [
"Lian Xu",
"Wanli Ouyang",
"Mohammed Bennamoun",
"Farid Boussaid",
"Dan Xu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Multi-Class_Token_Transformer_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Multi-Class_Token_Transformer_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Multi-Class_Token_Transformer_CVPR_2022_supplemental.pdf | 2203.02891 | cvf | @InProceedings{Xu_2022_CVPR,
author = {Xu, Lian and Ouyang, Wanli and Bennamoun, Mohammed and Boussaid, Farid and Xu, Dan},
title = {Multi-Class Token Transformer for Weakly Supervised Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired by the fact that the attended regions of the one-class token in the standard vision transformer can be leveraged to form a class-agnostic l... | [
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29 | 3D Moments From Near-Duplicate Photos | [
"Qianqian Wang",
"Zhengqi Li",
"David Salesin",
"Noah Snavely",
"Brian Curless",
"Janne Kontkanen"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_3D_Moments_From_Near-Duplicate_Photos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_3D_Moments_From_Near-Duplicate_Photos_CVPR_2022_paper.pdf | null | 2205.06255 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Qianqian and Li, Zhengqi and Salesin, David and Snavely, Noah and Curless, Brian and Kontkanen, Janne},
title = {3D Moments From Near-Duplicate Photos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | We introduce 3D Moments, a new computational photography effect. As input we take a pair of near-duplicate photos, i.e., photos of moving subjects from similar viewpoints, common in people's photo collections. As output, we produce a video that smoothly interpolates the scene motion from the first photo to the second, ... | [
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30 | Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization | [
"Yabin Zhang",
"Minghan Li",
"Ruihuang Li",
"Kui Jia",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exact_Feature_Distribution_Matching_for_Arbitrary_Style_Transfer_and_Domain_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Exact_Feature_Distribution_Matching_for_Arbitrary_Style_Transfer_and_Domain_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Exact_Feature_Distribution_CVPR_2022_supplemental.pdf | 2203.07740 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Yabin and Li, Minghan and Li, Ruihuang and Jia, Kui and Zhang, Lei},
title = {Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Arbitrary style transfer (AST) and ___domain generalization (DG) are important yet challenging visual learning tasks, which can be cast as a feature distribution matching problem. With the assumption of Gaussian feature distribution, conventional feature distribution matching methods usually match the mean and standard de... | [
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31 | Blind2Unblind: Self-Supervised Image Denoising With Visible Blind Spots | [
"Zejin Wang",
"Jiazheng Liu",
"Guoqing Li",
"Hua Han"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Blind2Unblind_Self-Supervised_Image_Denoising_With_Visible_Blind_Spots_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Blind2Unblind_Self-Supervised_Image_Denoising_With_Visible_Blind_Spots_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Blind2Unblind_Self-Supervised_Image_CVPR_2022_supplemental.pdf | 2203.06967 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Zejin and Liu, Jiazheng and Li, Guoqing and Han, Hua},
title = {Blind2Unblind: Self-Supervised Image Denoising With Visible Blind Spots},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile, supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised denoisers, which learn only from single noisy images, solve the data collection problem. However, self-supervised denoising methods, especiall... | [
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32 | Balanced and Hierarchical Relation Learning for One-Shot Object Detection | [
"Hanqing Yang",
"Sijia Cai",
"Hualian Sheng",
"Bing Deng",
"Jianqiang Huang",
"Xian-Sheng Hua",
"Yong Tang",
"Yu Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Balanced_and_Hierarchical_Relation_Learning_for_One-Shot_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Balanced_and_Hierarchical_Relation_Learning_for_One-Shot_Object_Detection_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Yang_2022_CVPR,
author = {Yang, Hanqing and Cai, Sijia and Sheng, Hualian and Deng, Bing and Huang, Jianqiang and Hua, Xian-Sheng and Tang, Yong and Zhang, Yu},
title = {Balanced and Hierarchical Relation Learning for One-Shot Object Detection},
booktitle = {Proceedings of the IEEE/CVF... | Instance-level feature matching is significantly important to the success of modern one-shot object detectors. Recently, the methods based on the metric-learning paradigm have achieved an impressive process. Most of these works only measure the relations between query and target objects on a single level, resulting in ... | [
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33 | End-to-End Generative Pretraining for Multimodal Video Captioning | [
"Paul Hongsuck Seo",
"Arsha Nagrani",
"Anurag Arnab",
"Cordelia Schmid"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Seo_End-to-End_Generative_Pretraining_for_Multimodal_Video_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Seo_End-to-End_Generative_Pretraining_for_Multimodal_Video_Captioning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Seo_End-to-End_Generative_Pretraining_CVPR_2022_supplemental.zip | 2201.08264 | cvf | @InProceedings{Seo_2022_CVPR,
author = {Seo, Paul Hongsuck and Nagrani, Arsha and Arnab, Anurag and Schmid, Cordelia},
title = {End-to-End Generative Pretraining for Multimodal Video Captioning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Recent video and language pretraining frameworks lack the ability to generate sentences. We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining framework for learning from unlabelled videos which can be effectively used for generative tasks such as multimodal video captioning. Unlike recent vide... | [
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34 | Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts | [
"Chongzhi Zhang",
"Mingyuan Zhang",
"Shanghang Zhang",
"Daisheng Jin",
"Qiang Zhou",
"Zhongang Cai",
"Haiyu Zhao",
"Xianglong Liu",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Delving_Deep_Into_the_Generalization_of_Vision_Transformers_Under_Distribution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Delving_Deep_Into_the_Generalization_of_Vision_Transformers_Under_Distribution_CVPR_2022_paper.pdf | null | 2106.07617 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Chongzhi and Zhang, Mingyuan and Zhang, Shanghang and Jin, Daisheng and Zhou, Qiang and Cai, Zhongang and Zhao, Haiyu and Liu, Xianglong and Liu, Ziwei},
title = {Delving Deep Into the Generalization of Vision Transformers Under Distribution Shifts},
b... | Recently, Vision Transformers have achieved impressive results on various Vision tasks. Yet, their generalization ability under different distribution shifts is poorly understood. In this work, we provide a comprehensive study on the out-of-distribution generalization of Vision Transformers. To support a systematic inv... | [
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35 | NICE-SLAM: Neural Implicit Scalable Encoding for SLAM | [
"Zihan Zhu",
"Songyou Peng",
"Viktor Larsson",
"Weiwei Xu",
"Hujun Bao",
"Zhaopeng Cui",
"Martin R. Oswald",
"Marc Pollefeys"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_NICE-SLAM_Neural_Implicit_Scalable_Encoding_for_SLAM_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_NICE-SLAM_Neural_Implicit_Scalable_Encoding_for_SLAM_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_NICE-SLAM_Neural_Implicit_CVPR_2022_supplemental.pdf | 2112.12130 | title_snapshot | @InProceedings{Zhu_2022_CVPR,
author = {Zhu, Zihan and Peng, Songyou and Larsson, Viktor and Xu, Weiwei and Bao, Hujun and Cui, Zhaopeng and Oswald, Martin R. and Pollefeys, Marc},
title = {NICE-SLAM: Neural Implicit Scalable Encoding for SLAM},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainl... | [
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36 | HyperDet3D: Learning a Scene-Conditioned 3D Object Detector | [
"Yu Zheng",
"Yueqi Duan",
"Jiwen Lu",
"Jie Zhou",
"Qi Tian"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_HyperDet3D_Learning_a_Scene-Conditioned_3D_Object_Detector_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_HyperDet3D_Learning_a_Scene-Conditioned_3D_Object_Detector_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_HyperDet3D_Learning_a_CVPR_2022_supplemental.pdf | 2204.05599 | cvf | @InProceedings{Zheng_2022_CVPR,
author = {Zheng, Yu and Duan, Yueqi and Lu, Jiwen and Zhou, Jie and Tian, Qi},
title = {HyperDet3D: Learning a Scene-Conditioned 3D Object Detector},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | A bathtub in a library, a sink in an office, a bed in a laundry room - the counter-intuition suggests that scene provides important prior knowledge for 3D object detection, which instructs to eliminate the ambiguous detection of similar objects. In this paper, we propose HyperDet3D to explore scene-conditioned prior kn... | [
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37 | Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion | [
"Tianpei Gu",
"Guangyi Chen",
"Junlong Li",
"Chunze Lin",
"Yongming Rao",
"Jie Zhou",
"Jiwen Lu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Stochastic_Trajectory_Prediction_via_Motion_Indeterminacy_Diffusion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_Stochastic_Trajectory_Prediction_via_Motion_Indeterminacy_Diffusion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gu_Stochastic_Trajectory_Prediction_CVPR_2022_supplemental.pdf | 2203.13777 | cvf | @InProceedings{Gu_2022_CVPR,
author = {Gu, Tianpei and Chen, Guangyi and Li, Junlong and Lin, Chunze and Rao, Yongming and Zhou, Jie and Lu, Jiwen},
title = {Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory prediction system to model the multi-modality of future motion states. Unlike existing stochastic trajectory prediction methods which usually use a latent variable to represent multi-modality, we explicitly simulate the process of ... | [
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38 | CLRNet: Cross Layer Refinement Network for Lane Detection | [
"Tu Zheng",
"Yifei Huang",
"Yang Liu",
"Wenjian Tang",
"Zheng Yang",
"Deng Cai",
"Xiaofei He"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_CLRNet_Cross_Layer_Refinement_Network_for_Lane_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_CLRNet_Cross_Layer_Refinement_Network_for_Lane_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_CLRNet_Cross_Layer_CVPR_2022_supplemental.pdf | 2203.10350 | cvf | @InProceedings{Zheng_2022_CVPR,
author = {Zheng, Tu and Huang, Yifei and Liu, Yang and Tang, Wenjian and Yang, Zheng and Cai, Deng and He, Xiaofei},
title = {CLRNet: Cross Layer Refinement Network for Lane Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great importance for accurate lane det... | [
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39 | Cross-Modal Map Learning for Vision and Language Navigation | [
"Georgios Georgakis",
"Karl Schmeckpeper",
"Karan Wanchoo",
"Soham Dan",
"Eleni Miltsakaki",
"Dan Roth",
"Kostas Daniilidis"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Georgakis_Cross-Modal_Map_Learning_for_Vision_and_Language_Navigation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Georgakis_Cross-Modal_Map_Learning_for_Vision_and_Language_Navigation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Georgakis_Cross-Modal_Map_Learning_CVPR_2022_supplemental.pdf | 2203.05137 | cvf | @InProceedings{Georgakis_2022_CVPR,
author = {Georgakis, Georgios and Schmeckpeper, Karl and Wanchoo, Karan and Dan, Soham and Miltsakaki, Eleni and Roth, Dan and Daniilidis, Kostas},
title = {Cross-Modal Map Learning for Vision and Language Navigation},
booktitle = {Proceedings of the IEEE/CVF Confe... | We consider the problem of Vision-and-Language Navigation (VLN). The majority of current methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or using cross-modal attention over the egocentric observations of the agent. In contrast to other works, our key insight is that the association... | [
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40 | Motion-Aware Contrastive Video Representation Learning via Foreground-Background Merging | [
"Shuangrui Ding",
"Maomao Li",
"Tianyu Yang",
"Rui Qian",
"Haohang Xu",
"Qingyi Chen",
"Jue Wang",
"Hongkai Xiong"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Motion-Aware_Contrastive_Video_Representation_Learning_via_Foreground-Background_Merging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Motion-Aware_Contrastive_Video_Representation_Learning_via_Foreground-Background_Merging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Motion-Aware_Contrastive_Video_CVPR_2022_supplemental.pdf | 2109.15130 | cvf | @InProceedings{Ding_2022_CVPR,
author = {Ding, Shuangrui and Li, Maomao and Yang, Tianyu and Qian, Rui and Xu, Haohang and Chen, Qingyi and Wang, Jue and Xiong, Hongkai},
title = {Motion-Aware Contrastive Video Representation Learning via Foreground-Background Merging},
booktitle = {Proceedings of th... | In light of the success of contrastive learning in the image ___domain, current self-supervised video representation learning methods usually employ contrastive loss to facilitate video representation learning. When naively pulling two augmented views of a video closer, the model however tends to learn the common static b... | [
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41 | Incremental Transformer Structure Enhanced Image Inpainting With Masking Positional Encoding | [
"Qiaole Dong",
"Chenjie Cao",
"Yanwei Fu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Incremental_Transformer_Structure_Enhanced_Image_Inpainting_With_Masking_Positional_Encoding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Incremental_Transformer_Structure_Enhanced_Image_Inpainting_With_Masking_Positional_Encoding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Incremental_Transformer_Structure_CVPR_2022_supplemental.pdf | 2203.00867 | cvf | @InProceedings{Dong_2022_CVPR,
author = {Dong, Qiaole and Cao, Chenjie and Fu, Yanwei},
title = {Incremental Transformer Structure Enhanced Image Inpainting With Masking Positional Encoding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | Image inpainting has made significant advances in recent years. However, it is still challenging to recover corrupted images with both vivid textures and reasonable structures. Some specific methods can only tackle regular textures while losing holistic structures due to the limited receptive fields of convolutional ne... | [
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42 | Pointly-Supervised Instance Segmentation | [
"Bowen Cheng",
"Omkar Parkhi",
"Alexander Kirillov"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cheng_Pointly-Supervised_Instance_Segmentation_CVPR_2022_supplemental.pdf | 2104.06404 | cvf | @InProceedings{Cheng_2022_CVPR,
author = {Cheng, Bowen and Parkhi, Omkar and Kirillov, Alexander},
title = {Pointly-Supervised Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022}... | We propose an embarrassingly simple point annotation scheme to collect weak supervision for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of points uniformly sampled inside each bounding box. We show that the existing instance segmentation models developed for full mask superv... | [
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43 | Cross-Modal Clinical Graph Transformer for Ophthalmic Report Generation | [
"Mingjie Li",
"Wenjia Cai",
"Karin Verspoor",
"Shirui Pan",
"Xiaodan Liang",
"Xiaojun Chang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Cross-Modal_Clinical_Graph_Transformer_for_Ophthalmic_Report_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Cross-Modal_Clinical_Graph_Transformer_for_Ophthalmic_Report_Generation_CVPR_2022_paper.pdf | null | 2206.01988 | title_snapshot | @InProceedings{Li_2022_CVPR,
author = {Li, Mingjie and Cai, Wenjia and Verspoor, Karin and Pan, Shirui and Liang, Xiaodan and Chang, Xiaojun},
title = {Cross-Modal Clinical Graph Transformer for Ophthalmic Report Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | Automatic generation of ophthalmic reports using data-driven neural networks has great potential in clinical practice. When writing a report, ophthalmologists make inferences with prior clinical knowledge. This knowledge has been neglected in prior medical report generation methods. To endow models with the capability ... | [
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44 | Human-Object Interaction Detection via Disentangled Transformer | [
"Desen Zhou",
"Zhichao Liu",
"Jian Wang",
"Leshan Wang",
"Tao Hu",
"Errui Ding",
"Jingdong Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Human-Object_Interaction_Detection_via_Disentangled_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Human-Object_Interaction_Detection_via_Disentangled_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Human-Object_Interaction_Detection_CVPR_2022_supplemental.pdf | 2204.09290 | cvf | @InProceedings{Zhou_2022_CVPR,
author = {Zhou, Desen and Liu, Zhichao and Wang, Jian and Wang, Leshan and Hu, Tao and Ding, Errui and Wang, Jingdong},
title = {Human-Object Interaction Detection via Disentangled Transformer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and ... | Human-Object Interaction Detection tackles the problem of joint localization and classification of human object interactions. Existing HOI transformers either adopt a single decoder for triplet prediction, or utilize two parallel decoders to detect individual objects and interactions separately, and compose triplets by... | [
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45 | DINE: Domain Adaptation From Single and Multiple Black-Box Predictors | [
"Jian Liang",
"Dapeng Hu",
"Jiashi Feng",
"Ran He"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_DINE_Domain_Adaptation_From_Single_and_Multiple_Black-Box_Predictors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_DINE_Domain_Adaptation_From_Single_and_Multiple_Black-Box_Predictors_CVPR_2022_paper.pdf | null | 2104.01539 | cvf | @InProceedings{Liang_2022_CVPR,
author = {Liang, Jian and Hu, Dapeng and Feng, Jiashi and He, Ran},
title = {DINE: Domain Adaptation From Single and Multiple Black-Box Predictors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | To ease the burden of labeling, unsupervised ___domain adaptation (UDA) aims to transfer knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset (target). Despite impressive progress, prior methods always need to access the raw source data and develop data-dependent alignment approaches to ... | [
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46 | LGT-Net: Indoor Panoramic Room Layout Estimation With Geometry-Aware Transformer Network | [
"Zhigang Jiang",
"Zhongzheng Xiang",
"Jinhua Xu",
"Ming Zhao"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_LGT-Net_Indoor_Panoramic_Room_Layout_Estimation_With_Geometry-Aware_Transformer_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_LGT-Net_Indoor_Panoramic_Room_Layout_Estimation_With_Geometry-Aware_Transformer_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_LGT-Net_Indoor_Panoramic_CVPR_2022_supplemental.pdf | 2203.01824 | title_snapshot | @InProceedings{Jiang_2022_CVPR,
author = {Jiang, Zhigang and Xiang, Zhongzheng and Xu, Jinhua and Zhao, Ming},
title = {LGT-Net: Indoor Panoramic Room Layout Estimation With Geometry-Aware Transformer Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | 3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient geometry awareness of room layout with the only latitude of boundaries or horizon-depth. We present that using horizon-depth along with room height can obtain omnidire... | [
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47 | CRIS: CLIP-Driven Referring Image Segmentation | [
"Zhaoqing Wang",
"Yu Lu",
"Qiang Li",
"Xunqiang Tao",
"Yandong Guo",
"Mingming Gong",
"Tongliang Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_CRIS_CLIP-Driven_Referring_Image_Segmentation_CVPR_2022_paper.pdf | null | 2111.15174 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Zhaoqing and Lu, Yu and Li, Qiang and Tao, Xunqiang and Guo, Yandong and Gong, Mingming and Liu, Tongliang},
title = {CRIS: CLIP-Driven Referring Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Referring image segmentation aims to segment a referent via a natural linguistic expression. Due to the distinct data properties between text and image, it is challenging for a network to well align text and pixel-level features. Existing approaches use pretrained models to facilitate learning, yet separately transfer ... | [
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48 | Multi-View Mesh Reconstruction With Neural Deferred Shading | [
"Markus Worchel",
"Rodrigo Diaz",
"Weiwen Hu",
"Oliver Schreer",
"Ingo Feldmann",
"Peter Eisert"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Worchel_Multi-View_Mesh_Reconstruction_With_Neural_Deferred_Shading_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Worchel_Multi-View_Mesh_Reconstruction_With_Neural_Deferred_Shading_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Worchel_Multi-View_Mesh_Reconstruction_CVPR_2022_supplemental.pdf | 2212.04386 | title_snapshot | @InProceedings{Worchel_2022_CVPR,
author = {Worchel, Markus and Diaz, Rodrigo and Hu, Weiwen and Schreer, Oliver and Feldmann, Ingo and Eisert, Peter},
title = {Multi-View Mesh Reconstruction With Neural Deferred Shading},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pat... | We propose an analysis-by-synthesis method for fast multi-view 3D reconstruction of opaque objects with arbitrary materials and illumination. State-of-the-art methods use both neural surface representations and neural rendering. While flexible, neural surface representations are a significant bottleneck in optimization... | [
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49 | CVF-SID: Cyclic Multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise From Image | [
"Reyhaneh Neshatavar",
"Mohsen Yavartanoo",
"Sanghyun Son",
"Kyoung Mu Lee"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Neshatavar_CVF-SID_Cyclic_Multi-Variate_Function_for_Self-Supervised_Image_Denoising_by_Disentangling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Neshatavar_CVF-SID_Cyclic_Multi-Variate_Function_for_Self-Supervised_Image_Denoising_by_Disentangling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Neshatavar_CVF-SID_Cyclic_Multi-Variate_CVPR_2022_supplemental.pdf | 2203.13009 | title_snapshot | @InProceedings{Neshatavar_2022_CVPR,
author = {Neshatavar, Reyhaneh and Yavartanoo, Mohsen and Son, Sanghyun and Lee, Kyoung Mu},
title = {CVF-SID: Cyclic Multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise From Image},
booktitle = {Proceedings of the IEEE/CVF Conference... | Recently, significant progress has been made on image denoising with strong supervision from large-scale datasets. However, obtaining well-aligned noisy-clean training image pairs for each specific scenario is complicated and costly in practice. Consequently, applying a conventional supervised denoising network on in-t... | [
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50 | Infrared Invisible Clothing: Hiding From Infrared Detectors at Multiple Angles in Real World | [
"Xiaopei Zhu",
"Zhanhao Hu",
"Siyuan Huang",
"Jianmin Li",
"Xiaolin Hu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Infrared_Invisible_Clothing_Hiding_From_Infrared_Detectors_at_Multiple_Angles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Infrared_Invisible_Clothing_Hiding_From_Infrared_Detectors_at_Multiple_Angles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Infrared_Invisible_Clothing_CVPR_2022_supplemental.zip | 2205.05909 | cvf | @InProceedings{Zhu_2022_CVPR,
author = {Zhu, Xiaopei and Hu, Zhanhao and Huang, Siyuan and Li, Jianmin and Hu, Xiaolin},
title = {Infrared Invisible Clothing: Hiding From Infrared Detectors at Multiple Angles in Real World},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We proposed the infrared adversarial clothing, which could fool infrared pedestrian detectors at different angles. We simulated the process from clot... | [
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51 | Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation | [
"Zitian Wang",
"Xuecheng Nie",
"Xiaochao Qu",
"Yunpeng Chen",
"Si Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Distribution-Aware_Single-Stage_Models_for_Multi-Person_3D_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Distribution-Aware_Single-Stage_Models_for_Multi-Person_3D_Pose_Estimation_CVPR_2022_paper.pdf | null | 2203.07697 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Zitian and Nie, Xuecheng and Qu, Xiaochao and Chen, Yunpeng and Liu, Si},
title = {Distribution-Aware Single-Stage Models for Multi-Person 3D Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition... | In this paper, we present a novel Distribution-Aware Single-stage (DAS) model for tackling the challenging multi-person 3D pose estimation problem. Different from existing top-down and bottom-up methods, the proposed DAS model simultaneously localizes person positions and their corresponding body joints in the 3D camer... | [
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52 | FaceFormer: Speech-Driven 3D Facial Animation With Transformers | [
"Yingruo Fan",
"Zhaojiang Lin",
"Jun Saito",
"Wenping Wang",
"Taku Komura"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_FaceFormer_Speech-Driven_3D_Facial_Animation_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_FaceFormer_Speech-Driven_3D_CVPR_2022_supplemental.zip | 2112.05329 | cvf | @InProceedings{Fan_2022_CVPR,
author = {Fan, Yingruo and Lin, Zhaojiang and Saito, Jun and Wang, Wenping and Komura, Taku},
title = {FaceFormer: Speech-Driven 3D Facial Animation With Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data. Prior works typically focus on learning phoneme-level features of short audio windows with limited context, occasionally resulting in inaccurate lip movements. To tackle this... | [
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53 | Exploring Patch-Wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks | [
"Chanyong Jung",
"Gihyun Kwon",
"Jong Chul Ye"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Jung_Exploring_Patch-Wise_Semantic_Relation_for_Contrastive_Learning_in_Image-to-Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Jung_Exploring_Patch-Wise_Semantic_Relation_for_Contrastive_Learning_in_Image-to-Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jung_Exploring_Patch-Wise_Semantic_CVPR_2022_supplemental.pdf | 2203.01532 | cvf | @InProceedings{Jung_2022_CVPR,
author = {Jung, Chanyong and Kwon, Gihyun and Ye, Jong Chul},
title = {Exploring Patch-Wise Semantic Relation for Contrastive Learning in Image-to-Image Translation Tasks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (C... | Recently, contrastive learning-based image translation methods have been proposed, which contrasts different spatial locations to enhance the spatial correspondence. However, the methods often ignore the diverse semantic relation within the images. To address this, here we propose a novel semantic relation consistency ... | [
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54 | High-Resolution Face Swapping via Latent Semantics Disentanglement | [
"Yangyang Xu",
"Bailin Deng",
"Junle Wang",
"Yanqing Jing",
"Jia Pan",
"Shengfeng He"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_High-Resolution_Face_Swapping_via_Latent_Semantics_Disentanglement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_High-Resolution_Face_Swapping_via_Latent_Semantics_Disentanglement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_High-Resolution_Face_Swapping_CVPR_2022_supplemental.pdf | 2203.15958 | cvf | @InProceedings{Xu_2022_CVPR,
author = {Xu, Yangyang and Deng, Bailin and Wang, Junle and Jing, Yanqing and Pan, Jia and He, Shengfeng},
title = {High-Resolution Face Swapping via Latent Semantics Disentanglement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitly disentangle the la... | [
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55 | Searching the Deployable Convolution Neural Networks for GPUs | [
"Linnan Wang",
"Chenhan Yu",
"Satish Salian",
"Slawomir Kierat",
"Szymon Migacz",
"Alex Fit Florea"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Searching_the_Deployable_Convolution_Neural_Networks_for_GPUs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Searching_the_Deployable_Convolution_Neural_Networks_for_GPUs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Searching_the_Deployable_CVPR_2022_supplemental.pdf | 2205.00841 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Linnan and Yu, Chenhan and Salian, Satish and Kierat, Slawomir and Migacz, Szymon and Florea, Alex Fit},
title = {Searching the Deployable Convolution Neural Networks for GPUs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Customizing Convolution Neural Networks (CNN) for production use has been a challenging task for DL practitioners. This paper intends to expedite the model customization with a model hub that contains the optimized models tiered by their inference latency using Neural Architecture Search (NAS). To achieve this goal, we... | [
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56 | Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning | [
"Jiahao Xia",
"Weiwei Qu",
"Wenjian Huang",
"Jianguo Zhang",
"Xi Wang",
"Min Xu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Sparse_Local_Patch_Transformer_for_Robust_Face_Alignment_and_Landmarks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xia_Sparse_Local_Patch_Transformer_for_Robust_Face_Alignment_and_Landmarks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xia_Sparse_Local_Patch_CVPR_2022_supplemental.pdf | 2203.06541 | cvf | @InProceedings{Xia_2022_CVPR,
author = {Xia, Jiahao and Qu, Weiwei and Huang, Wenjian and Zhang, Jianguo and Wang, Xi and Xu, Min},
title = {Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Compu... | Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a lo... | [
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57 | DeepFake Disrupter: The Detector of DeepFake Is My Friend | [
"Xueyu Wang",
"Jiajun Huang",
"Siqi Ma",
"Surya Nepal",
"Chang Xu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_DeepFake_Disrupter_The_Detector_of_DeepFake_Is_My_Friend_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_DeepFake_Disrupter_The_Detector_of_DeepFake_Is_My_Friend_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_DeepFake_Disrupter_The_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Wang_2022_CVPR,
author = {Wang, Xueyu and Huang, Jiajun and Ma, Siqi and Nepal, Surya and Xu, Chang},
title = {DeepFake Disrupter: The Detector of DeepFake Is My Friend},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | In recent years, with the advances of generative models, many powerful face manipulation systems have been developed based on Deep Neural Networks (DNNs), called DeepFakes. If DeepFakes are not controlled timely and properly, they would become a real threat to both celebrities and ordinary people. Precautions such as a... | [
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58 | Rotationally Equivariant 3D Object Detection | [
"Hong-Xing Yu",
"Jiajun Wu",
"Li Yi"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Rotationally_Equivariant_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Rotationally_Equivariant_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Rotationally_Equivariant_3D_CVPR_2022_supplemental.pdf | 2204.13630 | cvf | @InProceedings{Yu_2022_CVPR,
author = {Yu, Hong-Xing and Wu, Jiajun and Yi, Li},
title = {Rotationally Equivariant 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages ... | Rotation equivariance has recently become a strongly desired property in the 3D deep learning community. Yet most existing methods focus on equivariance regarding a global input rotation while ignoring the fact that rotation symmetry has its own spatial support. Specifically, we consider the object detection problem in... | [
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59 | Accelerating DETR Convergence via Semantic-Aligned Matching | [
"Gongjie Zhang",
"Zhipeng Luo",
"Yingchen Yu",
"Kaiwen Cui",
"Shijian Lu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Accelerating_DETR_Convergence_via_Semantic-Aligned_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Accelerating_DETR_Convergence_via_Semantic-Aligned_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Accelerating_DETR_Convergence_CVPR_2022_supplemental.pdf | 2203.06883 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Gongjie and Luo, Zhipeng and Yu, Yingchen and Cui, Kaiwen and Lu, Shijian},
title = {Accelerating DETR Convergence via Semantic-Aligned Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | The recently developed DEtection TRansformer (DETR) establishes a new object detection paradigm by eliminating a series of hand-crafted components. However, DETR suffers from extremely slow convergence, which increases the training cost significantly. We observe that the slow convergence is largely attributed to the co... | [
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60 | Long-Short Temporal Contrastive Learning of Video Transformers | [
"Jue Wang",
"Gedas Bertasius",
"Du Tran",
"Lorenzo Torresani"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Long-Short_Temporal_Contrastive_Learning_of_Video_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Long-Short_Temporal_Contrastive_Learning_of_Video_Transformers_CVPR_2022_paper.pdf | null | 2106.09212 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Jue and Bertasius, Gedas and Tran, Du and Torresani, Lorenzo},
title = {Long-Short Temporal Contrastive Learning of Video Transformers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Video transformers have recently emerged as a competitive alternative to 3D CNNs for video understanding. However, due to their large number of parameters and reduced inductive biases, these models require supervised pretraining on large-scale image datasets to achieve top performance. In this paper, we empirically dem... | [
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61 | Vision Transformer With Deformable Attention | [
"Zhuofan Xia",
"Xuran Pan",
"Shiji Song",
"Li Erran Li",
"Gao Huang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Xia_Vision_Transformer_With_Deformable_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Xia_Vision_Transformer_With_Deformable_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xia_Vision_Transformer_With_CVPR_2022_supplemental.pdf | 2201.00520 | cvf | @InProceedings{Xia_2022_CVPR,
author = {Xia, Zhuofan and Pan, Xuran and Song, Shiji and Li, Li Erran and Huang, Gao},
title = {Vision Transformer With Deformable Attention},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | Transformers have recently shown superior performances on various vision tasks. The large, sometimes even global, receptive field endows Transformer models with higher representation power over their CNN counterparts. Nevertheless, simply enlarging receptive field also gives rise to several concerns. On the one hand, u... | [
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62 | Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture | [
"Tanmay Gupta",
"Amita Kamath",
"Aniruddha Kembhavi",
"Derek Hoiem"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Gupta_Towards_General_Purpose_Vision_Systems_An_End-to-End_Task-Agnostic_Vision-Language_Architecture_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Gupta_Towards_General_Purpose_Vision_Systems_An_End-to-End_Task-Agnostic_Vision-Language_Architecture_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gupta_Towards_General_Purpose_CVPR_2022_supplemental.zip | null | null | @InProceedings{Gupta_2022_CVPR,
author = {Gupta, Tanmay and Kamath, Amita and Kembhavi, Aniruddha and Hoiem, Derek},
title = {Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and P... | Computer vision systems today are primarily N-purpose systems, designed and trained for a predefined set of tasks. Adapting such systems to new tasks is challenging and often requires non-trivial modifications to the network architecture (e.g. adding new output heads) or training process (e.g. adding new losses). To re... | [
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63 | Deep Vanishing Point Detection: Geometric Priors Make Dataset Variations Vanish | [
"Yancong Lin",
"Ruben Wiersma",
"Silvia L. Pintea",
"Klaus Hildebrandt",
"Elmar Eisemann",
"Jan C. van Gemert"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Deep_Vanishing_Point_Detection_Geometric_Priors_Make_Dataset_Variations_Vanish_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Deep_Vanishing_Point_Detection_Geometric_Priors_Make_Dataset_Variations_Vanish_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Deep_Vanishing_Point_CVPR_2022_supplemental.pdf | 2203.08586 | cvf | @InProceedings{Lin_2022_CVPR,
author = {Lin, Yancong and Wiersma, Ruben and Pintea, Silvia L. and Hildebrandt, Klaus and Eisemann, Elmar and van Gemert, Jan C.},
title = {Deep Vanishing Point Detection: Geometric Priors Make Dataset Variations Vanish},
booktitle = {Proceedings of the IEEE/CVF Confere... | Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware and do not generalize to even slightly different domains, and minor problem variants. Here, we address these issues by injecting deep vanishing point detection networks with... | [
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64 | RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic Scenes | [
"Tak-Wai Hui"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Hui_RM-Depth_Unsupervised_Learning_of_Recurrent_Monocular_Depth_in_Dynamic_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Hui_RM-Depth_Unsupervised_Learning_of_Recurrent_Monocular_Depth_in_Dynamic_Scenes_CVPR_2022_paper.pdf | null | 2303.04456 | title_snapshot | @InProceedings{Hui_2022_CVPR,
author = {Hui, Tak-Wai},
title = {RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
... | Unsupervised methods have showed promising results on monocular depth estimation. However, the training data must be captured in scenes without moving objects. To push the envelope of accuracy, recent methods tend to increase their model parameters. In this paper, an unsupervised learning framework is proposed to joint... | [
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65 | LiT: Zero-Shot Transfer With Locked-Image Text Tuning | [
"Xiaohua Zhai",
"Xiao Wang",
"Basil Mustafa",
"Andreas Steiner",
"Daniel Keysers",
"Alexander Kolesnikov",
"Lucas Beyer"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhai_LiT_Zero-Shot_Transfer_CVPR_2022_supplemental.pdf | 2111.07991 | cvf | @InProceedings{Zhai_2022_CVPR,
author = {Zhai, Xiaohua and Wang, Xiao and Mustafa, Basil and Steiner, Andreas and Keysers, Daniel and Kolesnikov, Alexander and Beyer, Lucas},
title = {LiT: Zero-Shot Transfer With Locked-Image Text Tuning},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training. In our empirical study we find that locked pre-trained image models with unlocked text models work best. We call this instance of contrastive-tuning "... | [
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66 | Cloning Outfits From Real-World Images to 3D Characters for Generalizable Person Re-Identification | [
"Yanan Wang",
"Xuezhi Liang",
"Shengcai Liao"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Cloning_Outfits_From_Real-World_Images_to_3D_Characters_for_Generalizable_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Cloning_Outfits_From_Real-World_Images_to_3D_Characters_for_Generalizable_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Cloning_Outfits_From_CVPR_2022_supplemental.pdf | 2204.02611 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Yanan and Liang, Xuezhi and Liao, Shengcai},
title = {Cloning Outfits From Real-World Images to 3D Characters for Generalizable Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Recently, large-scale synthetic datasets are shown to be very useful for generalizable person re-identification. However, synthesized persons in existing datasets are mostly cartoon-like and in random dress collocation, which limits their performance. To address this, in this work, an automatic approach is proposed to ... | [
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67 | GeoNeRF: Generalizing NeRF With Geometry Priors | [
"Mohammad Mahdi Johari",
"Yann Lepoittevin",
"François Fleuret"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Johari_GeoNeRF_Generalizing_NeRF_With_Geometry_Priors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Johari_GeoNeRF_Generalizing_NeRF_With_Geometry_Priors_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Johari_GeoNeRF_Generalizing_NeRF_CVPR_2022_supplemental.pdf | 2111.13539 | cvf | @InProceedings{Johari_2022_CVPR,
author = {Johari, Mohammad Mahdi and Lepoittevin, Yann and Fleuret, Fran\c{c}ois},
title = {GeoNeRF: Generalizing NeRF With Geometry Priors},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June}... | We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a renderer. To render a novel view, the geometry reasoner first constructs cascaded cost volumes for each nearby source view. Then, using a Tr... | [
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68 | ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo | [
"Biwen Lei",
"Xiefan Guo",
"Hongyu Yang",
"Miaomiao Cui",
"Xuansong Xie",
"Di Huang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Lei_ABPN_Adaptive_Blend_Pyramid_Network_for_Real-Time_Local_Retouching_of_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lei_ABPN_Adaptive_Blend_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Lei_2022_CVPR,
author = {Lei, Biwen and Guo, Xiefan and Yang, Hongyu and Cui, Miaomiao and Xie, Xuansong and Huang, Di},
title = {ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | Photo retouching finds many applications in various fields. However, most existing methods are designed for global retouching and seldom pay attention to the local region, while the latter is actually much more tedious and time-consuming in photography pipelines. In this paper, we propose a novel adaptive blend pyramid... | [
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69 | PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation With Photometrically Challenging Objects | [
"Pengyuan Wang",
"HyunJun Jung",
"Yitong Li",
"Siyuan Shen",
"Rahul Parthasarathy Srikanth",
"Lorenzo Garattoni",
"Sven Meier",
"Nassir Navab",
"Benjamin Busam"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PhoCaL_A_Multi-Modal_Dataset_for_Category-Level_Object_Pose_Estimation_With_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_PhoCaL_A_Multi-Modal_Dataset_for_Category-Level_Object_Pose_Estimation_With_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_PhoCaL_A_Multi-Modal_CVPR_2022_supplemental.zip | 2205.08811 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Pengyuan and Jung, HyunJun and Li, Yitong and Shen, Siyuan and Srikanth, Rahul Parthasarathy and Garattoni, Lorenzo and Meier, Sven and Navab, Nassir and Busam, Benjamin},
title = {PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation With ... | Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape has become a promising trend. As such, a new research field needs to be supported by well-designed datasets. To provide a benchmark with hig... | [
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70 | Neural Compression-Based Feature Learning for Video Restoration | [
"Cong Huang",
"Jiahao Li",
"Bin Li",
"Dong Liu",
"Yan Lu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Neural_Compression-Based_Feature_CVPR_2022_supplemental.pdf | 2203.09208 | cvf | @InProceedings{Huang_2022_CVPR,
author = {Huang, Cong and Li, Jiahao and Li, Bin and Liu, Dong and Lu, Yan},
title = {Neural Compression-Based Feature Learning for Video Restoration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Most existing deep learning (DL)-based video restoration methods focus on the network structure design to better extract temporal features but ignore how to utilize these extracted temporal features efficiently. The temporal features usually contain various noisy and irrelative information, and they may interfere with ... | [
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71 | Expanding Low-Density Latent Regions for Open-Set Object Detection | [
"Jiaming Han",
"Yuqiang Ren",
"Jian Ding",
"Xingjia Pan",
"Ke Yan",
"Gui-Song Xia"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Expanding_Low-Density_Latent_Regions_for_Open-Set_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Expanding_Low-Density_Latent_Regions_for_Open-Set_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Expanding_Low-Density_Latent_CVPR_2022_supplemental.pdf | 2203.14911 | cvf | @InProceedings{Han_2022_CVPR,
author = {Han, Jiaming and Ren, Yuqiang and Ding, Jian and Pan, Xingjia and Yan, Ke and Xia, Gui-Song},
title = {Expanding Low-Density Latent Regions for Open-Set Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Modern object detectors have achieved impressive progress under the close-set setup. However, open-set object detection (OSOD) remains challenging since objects of unknown categories are often misclassified to existing known classes. In this work, we propose to identify unknown objects by separating high/low-density re... | [
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72 | Drop the GAN: In Defense of Patches Nearest Neighbors As Single Image Generative Models | [
"Niv Granot",
"Ben Feinstein",
"Assaf Shocher",
"Shai Bagon",
"Michal Irani"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Granot_Drop_the_GAN_In_Defense_of_Patches_Nearest_Neighbors_As_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Granot_Drop_the_GAN_In_Defense_of_Patches_Nearest_Neighbors_As_CVPR_2022_paper.pdf | null | 2103.15545 | cvf | @InProceedings{Granot_2022_CVPR,
author = {Granot, Niv and Feinstein, Ben and Shocher, Assaf and Bagon, Shai and Irani, Michal},
title = {Drop the GAN: In Defense of Patches Nearest Neighbors As Single Image Generative Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | Image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks. Moreover, they off... | [
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73 | Uformer: A General U-Shaped Transformer for Image Restoration | [
"Zhendong Wang",
"Xiaodong Cun",
"Jianmin Bao",
"Wengang Zhou",
"Jianzhuang Liu",
"Houqiang Li"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Uformer_A_General_U-Shaped_Transformer_for_Image_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Uformer_A_General_U-Shaped_Transformer_for_Image_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Uformer_A_General_CVPR_2022_supplemental.pdf | 2106.03106 | title_snapshot | @InProceedings{Wang_2022_CVPR,
author = {Wang, Zhendong and Cun, Xiaodong and Bao, Jianmin and Zhou, Wengang and Liu, Jianzhuang and Li, Houqiang},
title = {Uformer: A General U-Shaped Transformer for Image Restoration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using the Transformer block. In Uformer, there are two core designs. First, we introduce a novel locally-enhanced window (LeWin) Transformer block,... | [
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74 | Exploring Dual-Task Correlation for Pose Guided Person Image Generation | [
"Pengze Zhang",
"Lingxiao Yang",
"Jian-Huang Lai",
"Xiaohua Xie"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exploring_Dual-Task_Correlation_for_Pose_Guided_Person_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Exploring_Dual-Task_Correlation_for_Pose_Guided_Person_Image_Generation_CVPR_2022_paper.pdf | null | 2203.02910 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Pengze and Yang, Lingxiao and Lai, Jian-Huang and Xie, Xiaohua},
title = {Exploring Dual-Task Correlation for Pose Guided Person Image Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Pose Guided Person Image Generation (PGPIG) is the task of transforming a person image from the source pose to a given target pose. Most of the existing methods only focus on the ill-posed source-to-target task and fail to capture reasonable texture mapping. To address this problem, we propose a novel Dual-task Pose Tr... | [
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75 | Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data | [
"Junfeng Lyu",
"Zhibo Wang",
"Feng Xu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Lyu_Portrait_Eyeglasses_and_Shadow_Removal_by_Leveraging_3D_Synthetic_Data_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Lyu_Portrait_Eyeglasses_and_Shadow_Removal_by_Leveraging_3D_Synthetic_Data_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lyu_Portrait_Eyeglasses_and_CVPR_2022_supplemental.zip | 2203.10474 | cvf | @InProceedings{Lyu_2022_CVPR,
author = {Lyu, Junfeng and Wang, Zhibo and Xu, Feng},
title = {Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | In portraits, eyeglasses may occlude facial regions and generate cast shadows on faces, which degrades the performance of many techniques like face verification and expression recognition. Portrait eyeglasses removal is critical in handling these problems. However, completely removing the eyeglasses is challenging beca... | [
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76 | Neural Rays for Occlusion-Aware Image-Based Rendering | [
"Yuan Liu",
"Sida Peng",
"Lingjie Liu",
"Qianqian Wang",
"Peng Wang",
"Christian Theobalt",
"Xiaowei Zhou",
"Wenping Wang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Neural_Rays_for_Occlusion-Aware_Image-Based_Rendering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Neural_Rays_for_Occlusion-Aware_Image-Based_Rendering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Neural_Rays_for_CVPR_2022_supplemental.pdf | 2107.13421 | cvf | @InProceedings{Liu_2022_CVPR,
author = {Liu, Yuan and Peng, Sida and Liu, Lingjie and Wang, Qianqian and Wang, Peng and Theobalt, Christian and Zhou, Xiaowei and Wang, Wenping},
title = {Neural Rays for Occlusion-Aware Image-Based Rendering},
booktitle = {Proceedings of the IEEE/CVF Conference on Com... | We present a new neural representation, called Neural Ray (NeuRay), for the novel view synthesis task. Recent works construct radiance fields from image features of input views to render novel view images, which enables the generalization to new scenes. However, due to occlusions, a 3D point may be invisible to some in... | [
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77 | Modeling 3D Layout for Group Re-Identification | [
"Quan Zhang",
"Kaiheng Dang",
"Jian-Huang Lai",
"Zhanxiang Feng",
"Xiaohua Xie"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Modeling_3D_Layout_for_Group_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Modeling_3D_Layout_for_Group_Re-Identification_CVPR_2022_paper.pdf | null | null | null | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Quan and Dang, Kaiheng and Lai, Jian-Huang and Feng, Zhanxiang and Xie, Xiaohua},
title = {Modeling 3D Layout for Group Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mont... | Group re-identification (GReID) attempts to correctly associate groups with the same members under different cameras. The main challenge is how to resist the membership and layout variations. Existing works attempt to incorporate layout modeling on the basis of appearance features to achieve robust group representation... | [
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78 | Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity | [
"Weiyao Wang",
"Matt Feiszli",
"Heng Wang",
"Jitendra Malik",
"Du Tran"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Open-World_Instance_Segmentation_Exploiting_Pseudo_Ground_Truth_From_Learned_Pairwise_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Open-World_Instance_Segmentation_Exploiting_Pseudo_Ground_Truth_From_Learned_Pairwise_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Open-World_Instance_Segmentation_CVPR_2022_supplemental.pdf | 2204.06107 | cvf | @InProceedings{Wang_2022_CVPR,
author = {Wang, Weiyao and Feiszli, Matt and Wang, Heng and Malik, Jitendra and Tran, Du},
title = {Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | Open-world instance segmentation is the task of grouping pixels into object instances without any pre-determined taxonomy. This is challenging, as state-of-the-art methods rely on explicit class semantics obtained from large labeled datasets, and out-of-___domain evaluation performance drops significantly. Here we propose... | [
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79 | SIOD: Single Instance Annotated per Category per Image for Object Detection | [
"Hanjun Li",
"Xingjia Pan",
"Ke Yan",
"Fan Tang",
"Wei-Shi Zheng"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_SIOD_Single_Instance_CVPR_2022_supplemental.pdf | 2203.15353 | cvf | @InProceedings{Li_2022_CVPR,
author = {Li, Hanjun and Pan, Xingjia and Yan, Ke and Tang, Fan and Zheng, Wei-Shi},
title = {SIOD: Single Instance Annotated per Category per Image for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR... | Object detection under imperfect data receives great attention recently. Weakly supervised object detection (WSOD) suffers from severe localization issues due to the lack of instance-level annotation, while semi-supervised object detection (SSOD) remains challenging led by the inter-image discrepancy between labeled an... | [
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80 | Toward Fast, Flexible, and Robust Low-Light Image Enhancement | [
"Long Ma",
"Tengyu Ma",
"Risheng Liu",
"Xin Fan",
"Zhongxuan Luo"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Toward_Fast_Flexible_and_Robust_Low-Light_Image_Enhancement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Toward_Fast_Flexible_and_Robust_Low-Light_Image_Enhancement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Toward_Fast_Flexible_CVPR_2022_supplemental.pdf | 2204.10137 | cvf | @InProceedings{Ma_2022_CVPR,
author = {Ma, Long and Ma, Tengyu and Liu, Risheng and Fan, Xin and Luo, Zhongxuan},
title = {Toward Fast, Flexible, and Robust Low-Light Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown complex scenarios. In this paper, we develop a new Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and robust brighte... | [
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81 | Online Learning of Reusable Abstract Models for Object Goal Navigation | [
"Tommaso Campari",
"Leonardo Lamanna",
"Paolo Traverso",
"Luciano Serafini",
"Lamberto Ballan"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Campari_Online_Learning_of_Reusable_Abstract_Models_for_Object_Goal_Navigation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Campari_Online_Learning_of_Reusable_Abstract_Models_for_Object_Goal_Navigation_CVPR_2022_paper.pdf | null | 2203.02583 | cvf | @InProceedings{Campari_2022_CVPR,
author = {Campari, Tommaso and Lamanna, Leonardo and Traverso, Paolo and Serafini, Luciano and Ballan, Lamberto},
title = {Online Learning of Reusable Abstract Models for Object Goal Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision ... | In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task. The Abstract Model is a finite state machine in which each state is an abstraction of a state of the environment,... | [
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82 | Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos | [
"Muheng Li",
"Lei Chen",
"Yueqi Duan",
"Zhilan Hu",
"Jianjiang Feng",
"Jie Zhou",
"Jiwen Lu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Bridge-Prompt_Towards_Ordinal_Action_Understanding_in_Instructional_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Bridge-Prompt_Towards_Ordinal_Action_Understanding_in_Instructional_Videos_CVPR_2022_paper.pdf | null | 2203.14104 | title_snapshot | @InProceedings{Li_2022_CVPR,
author = {Li, Muheng and Chen, Lei and Duan, Yueqi and Hu, Zhilan and Feng, Jianjiang and Zhou, Jie and Lu, Jiwen},
title = {Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Action recognition models have shown a promising capability to classify human actions in short video clips. In a real scenario, multiple correlated human actions commonly occur in particular orders, forming semantically meaningful human activities. Conventional action recognition approaches focus on analyzing single ac... | [
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83 | SimMatch: Semi-Supervised Learning With Similarity Matching | [
"Mingkai Zheng",
"Shan You",
"Lang Huang",
"Fei Wang",
"Chen Qian",
"Chang Xu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_SimMatch_Semi-Supervised_Learning_With_Similarity_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_SimMatch_Semi-Supervised_Learning_With_Similarity_Matching_CVPR_2022_paper.pdf | null | 2203.06915 | cvf | @InProceedings{Zheng_2022_CVPR,
author = {Zheng, Mingkai and You, Shan and Huang, Lang and Wang, Fei and Qian, Chen and Xu, Chang},
title = {SimMatch: Semi-Supervised Learning With Similarity Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. In SimMatch, the consistency regular... | [
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84 | OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks | [
"Wanyu Lin",
"Hao Lan",
"Hao Wang",
"Baochun Li"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_OrphicX_A_Causality-Inspired_Latent_Variable_Model_for_Interpreting_Graph_Neural_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_OrphicX_A_Causality-Inspired_Latent_Variable_Model_for_Interpreting_Graph_Neural_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_OrphicX_A_Causality-Inspired_CVPR_2022_supplemental.pdf | 2203.15209 | cvf | @InProceedings{Lin_2022_CVPR,
author = {Lin, Wanyu and Lan, Hao and Wang, Hao and Li, Baochun},
title = {OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Specifically, we construct a distinct generative model and design an objective function that encourages the generative model to produce causal,... | [
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85 | HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network | [
"JoonKyu Park",
"Yeonguk Oh",
"Gyeongsik Moon",
"Hongsuk Choi",
"Kyoung Mu Lee"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Park_HandOccNet_Occlusion-Robust_3D_Hand_Mesh_Estimation_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_HandOccNet_Occlusion-Robust_3D_Hand_Mesh_Estimation_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_HandOccNet_Occlusion-Robust_3D_CVPR_2022_supplemental.pdf | 2203.14564 | cvf | @InProceedings{Park_2022_CVPR,
author = {Park, JoonKyu and Oh, Yeonguk and Moon, Gyeongsik and Choi, Hongsuk and Lee, Kyoung Mu},
title = {HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | Hands are often severely occluded by objects, which makes 3D hand mesh estimation challenging. Previous works often have disregarded information at occluded regions. However, we argue that occluded regions have strong correlations with hands so that they can provide highly beneficial information for complete 3D hand me... | [
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86 | EfficientNeRF Efficient Neural Radiance Fields | [
"Tao Hu",
"Shu Liu",
"Yilun Chen",
"Tiancheng Shen",
"Jiaya Jia"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_EfficientNeRF__Efficient_Neural_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_EfficientNeRF__Efficient_Neural_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_EfficientNeRF__Efficient_CVPR_2022_supplemental.pdf | 2206.00878 | cvf | @InProceedings{Hu_2022_CVPR,
author = {Hu, Tao and Liu, Shu and Chen, Yilun and Shen, Tiancheng and Jia, Jiaya},
title = {EfficientNeRF Efficient Neural Radiance Fields},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes. It takes long per-scene training time and per-image testing time. In this paper, we present EfficientNeRF as an efficient NeRF-based method to represent 3D scene and synthesize novel-view images. Alt... | [
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87 | Quantifying Societal Bias Amplification in Image Captioning | [
"Yusuke Hirota",
"Yuta Nakashima",
"Noa Garcia"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Hirota_Quantifying_Societal_Bias_Amplification_in_Image_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Hirota_Quantifying_Societal_Bias_Amplification_in_Image_Captioning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hirota_Quantifying_Societal_Bias_CVPR_2022_supplemental.pdf | 2203.15395 | cvf | @InProceedings{Hirota_2022_CVPR,
author = {Hirota, Yusuke and Nakashima, Yuta and Garcia, Noa},
title = {Quantifying Societal Bias Amplification in Image Captioning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
ye... | We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and evaluate the societal bias in captions are not yet standardized. We provide a comprehensive study on the strengths and limitations of each ... | [
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88 | Modular Action Concept Grounding in Semantic Video Prediction | [
"Wei Yu",
"Wenxin Chen",
"Songheng Yin",
"Steve Easterbrook",
"Animesh Garg"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Modular_Action_Concept_Grounding_in_Semantic_Video_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Modular_Action_Concept_Grounding_in_Semantic_Video_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Modular_Action_Concept_CVPR_2022_supplemental.pdf | 2011.11201 | cvf | @InProceedings{Yu_2022_CVPR,
author = {Yu, Wei and Chen, Wenxin and Yin, Songheng and Easterbrook, Steve and Garg, Animesh},
title = {Modular Action Concept Grounding in Semantic Video Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},... | Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the learning of interaction between agents and objects. We introduce the task of semantic action-conditional video prediction, which uses semantic action labels to describe those inte... | [
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89 | StyleSwin: Transformer-Based GAN for High-Resolution Image Generation | [
"Bowen Zhang",
"Shuyang Gu",
"Bo Zhang",
"Jianmin Bao",
"Dong Chen",
"Fang Wen",
"Yong Wang",
"Baining Guo"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_StyleSwin_Transformer-Based_GAN_for_High-Resolution_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_StyleSwin_Transformer-Based_GAN_for_High-Resolution_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_StyleSwin_Transformer-Based_GAN_CVPR_2022_supplemental.pdf | 2112.10762 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Bowen and Gu, Shuyang and Zhang, Bo and Bao, Jianmin and Chen, Dong and Wen, Fang and Wang, Yong and Guo, Baining},
title = {StyleSwin: Transformer-Based GAN for High-Resolution Image Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on ... | Despite the tantalizing success in a broad of vision tasks, transformers have not yet demonstrated on-par ability as ConvNets in high-resolution image generative modeling. In this paper, we seek to explore using pure transformers to build a generative adversarial network for high-resolution image synthesis. To this end... | [
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90 | Reinforced Structured State-Evolution for Vision-Language Navigation | [
"Jinyu Chen",
"Chen Gao",
"Erli Meng",
"Qiong Zhang",
"Si Liu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Reinforced_Structured_State-Evolution_for_Vision-Language_Navigation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Reinforced_Structured_State-Evolution_for_Vision-Language_Navigation_CVPR_2022_paper.pdf | null | 2204.09280 | cvf | @InProceedings{Chen_2022_CVPR,
author = {Chen, Jinyu and Gao, Chen and Meng, Erli and Zhang, Qiong and Liu, Si},
title = {Reinforced Structured State-Evolution for Vision-Language Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Vision-and-language Navigation (VLN) task requires an embodied agent to navigate to a remote ___location following a natural language instruction. Previous methods usually adopt a sequence model (e.g., Transformer and LSTM) as the navigator. In such a paradigm, the sequence model predicts action at each step through a mai... | [
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91 | Sub-Word Level Lip Reading With Visual Attention | [
"K R Prajwal",
"Triantafyllos Afouras",
"Andrew Zisserman"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Prajwal_Sub-Word_Level_Lip_Reading_With_Visual_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Prajwal_Sub-Word_Level_Lip_Reading_With_Visual_Attention_CVPR_2022_paper.pdf | null | 2110.07603 | cvf | @InProceedings{Prajwal_2022_CVPR,
author = {Prajwal, K R and Afouras, Triantafyllos and Zisserman, Andrew},
title = {Sub-Word Level Lip Reading With Visual Attention},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | The goal of this paper is to learn strong lip reading models that can recognise speech in silent videos. Most prior works deal with the open-set visual speech recognition problem by adapting existing automatic speech recognition techniques on top of trivially pooled visual features. Instead, in this paper, we focus on ... | [
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92 | Weakly Supervised High-Fidelity Clothing Model Generation | [
"Ruili Feng",
"Cheng Ma",
"Chengji Shen",
"Xin Gao",
"Zhenjiang Liu",
"Xiaobo Li",
"Kairi Ou",
"Deli Zhao",
"Zheng-Jun Zha"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Weakly_Supervised_High-Fidelity_Clothing_Model_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_Weakly_Supervised_High-Fidelity_Clothing_Model_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Feng_Weakly_Supervised_High-Fidelity_CVPR_2022_supplemental.pdf | 2112.07200 | cvf | @InProceedings{Feng_2022_CVPR,
author = {Feng, Ruili and Ma, Cheng and Shen, Chengji and Gao, Xin and Liu, Zhenjiang and Li, Xiaobo and Ou, Kairi and Zhao, Deli and Zha, Zheng-Jun},
title = {Weakly Supervised High-Fidelity Clothing Model Generation},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales. However, the expensive proprietary model images challenge the existing image virtual try-on methods in this scenario, as most of them need to be trained on considerable amo... | [
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93 | Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph | [
"Siwei Wang",
"Xinwang Liu",
"Li Liu",
"Wenxuan Tu",
"Xinzhong Zhu",
"Jiyuan Liu",
"Sihang Zhou",
"En Zhu"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Highly-Efficient_Incomplete_Large-Scale_Multi-View_Clustering_With_Consensus_Bipartite_Graph_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Highly-Efficient_Incomplete_Large-Scale_Multi-View_Clustering_With_Consensus_Bipartite_Graph_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Highly-Efficient_Incomplete_Large-Scale_CVPR_2022_supplemental.pdf | null | null | @InProceedings{Wang_2022_CVPR,
author = {Wang, Siwei and Liu, Xinwang and Liu, Li and Tu, Wenxuan and Zhu, Xinzhong and Liu, Jiyuan and Zhou, Sihang and Zhu, En},
title = {Highly-Efficient Incomplete Large-Scale Multi-View Clustering With Consensus Bipartite Graph},
booktitle = {Proceedings of the IE... | Multi-view clustering has received increasing attention due to its effectiveness in fusing complementary information without manual annotations. Most previous methods hold the assumption that each instance appears in all views. However, it is not uncommon to see that some views may contain some missing instances, which... | [
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94 | Towards Principled Disentanglement for Domain Generalization | [
"Hanlin Zhang",
"Yi-Fan Zhang",
"Weiyang Liu",
"Adrian Weller",
"Bernhard Schölkopf",
"Eric P. Xing"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Towards_Principled_Disentanglement_for_Domain_Generalization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Towards_Principled_Disentanglement_for_Domain_Generalization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Towards_Principled_Disentanglement_CVPR_2022_supplemental.pdf | 2111.13839 | cvf | @InProceedings{Zhang_2022_CVPR,
author = {Zhang, Hanlin and Zhang, Yi-Fan and Liu, Weiyang and Weller, Adrian and Sch\"olkopf, Bernhard and Xing, Eric P.},
title = {Towards Principled Disentanglement for Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision an... | A fundamental challenge for machine learning models is generalizing to out-of-distribution (OOD) data, in part due to spurious correlations. To tackle this challenge, we first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG). We relax t... | [
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95 | Discrete Cosine Transform Network for Guided Depth Map Super-Resolution | [
"Zixiang Zhao",
"Jiangshe Zhang",
"Shuang Xu",
"Zudi Lin",
"Hanspeter Pfister"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Discrete_Cosine_Transform_Network_for_Guided_Depth_Map_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Discrete_Cosine_Transform_Network_for_Guided_Depth_Map_Super-Resolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Discrete_Cosine_Transform_CVPR_2022_supplemental.pdf | 2104.06977 | cvf | @InProceedings{Zhao_2022_CVPR,
author = {Zhao, Zixiang and Zhang, Jiangshe and Xu, Shuang and Lin, Zudi and Pfister, Hanspeter},
title = {Discrete Cosine Transform Network for Guided Depth Map Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogn... | Guided depth super-resolution (GDSR) is an essential topic in multi-modal image processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones collected with suboptimal conditions with the help of HR RGB images of the same scene. To solve the challenges in interpreting the working mechanism, ex... | [
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96 | Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing | [
"Xiaoxue Chen",
"Tianyu Liu",
"Hao Zhao",
"Guyue Zhou",
"Ya-Qin Zhang"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Cerberus_Transformer_Joint_Semantic_Affordance_and_Attribute_Parsing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Cerberus_Transformer_Joint_Semantic_Affordance_and_Attribute_Parsing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Cerberus_Transformer_Joint_CVPR_2022_supplemental.pdf | 2111.12608 | cvf | @InProceedings{Chen_2022_CVPR,
author = {Chen, Xiaoxue and Liu, Tianyu and Zhao, Hao and Zhou, Guyue and Zhang, Ya-Qin},
title = {Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance. In this paper, we tackle the new problem of joint semantic, affordance and attribute parsing. However, successfully resolving it requires a model to capture long-... | [
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97 | E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations | [
"Jongwan Kim",
"DongJin Lee",
"Byunggook Na",
"Seongsik Park",
"Sungroh Yoon"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_E2V-SDE_From_Asynchronous_Events_to_Fast_and_Continuous_Video_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_E2V-SDE_From_Asynchronous_Events_to_Fast_and_Continuous_Video_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_E2V-SDE_From_Asynchronous_CVPR_2022_supplemental.pdf | 2206.07578 | title_snapshot | @InProceedings{Kim_2022_CVPR,
author = {Kim, Jongwan and Lee, DongJin and Na, Byunggook and Park, Seongsik and Yoon, Sungroh},
title = {E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations},
booktitle = {Proceedings of the IEEE/CVF... | null | [
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98 | CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning | [
"Yue Fan",
"Dengxin Dai",
"Anna Kukleva",
"Bernt Schiele"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_CoSSL_Co-Learning_of_Representation_and_Classifier_for_Imbalanced_Semi-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_CoSSL_Co-Learning_of_Representation_and_Classifier_for_Imbalanced_Semi-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_CoSSL_Co-Learning_of_CVPR_2022_supplemental.pdf | 2112.04564 | cvf | @InProceedings{Fan_2022_CVPR,
author = {Fan, Yue and Dai, Dengxin and Kukleva, Anna and Schiele, Bernt},
title = {CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognitio... | Standard semi-supervised learning (SSL) using class-balanced datasets has shown great progress to leverage unlabeled data effectively. However, the more realistic setting of class-imbalanced data - called imbalanced SSL - is largely underexplored and standard SSL tends to underperform. In this paper, we propose a novel... | [
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99 | Discovering Objects That Can Move | [
"Zhipeng Bao",
"Pavel Tokmakov",
"Allan Jabri",
"Yu-Xiong Wang",
"Adrien Gaidon",
"Martial Hebert"
] | https://openaccess.thecvf.com/content/CVPR2022/html/Bao_Discovering_Objects_That_Can_Move_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Bao_Discovering_Objects_That_Can_Move_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bao_Discovering_Objects_That_CVPR_2022_supplemental.pdf | 2203.10159 | cvf | @InProceedings{Bao_2022_CVPR,
author = {Bao, Zhipeng and Tokmakov, Pavel and Jabri, Allan and Wang, Yu-Xiong and Gaidon, Adrien and Hebert, Martial},
title = {Discovering Objects That Can Move},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | This paper studies the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and ___location, to group pixels into object-like regions. However, by relying on appearance alone, these methods fail to separate objects... | [
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