CVPR
Collection
Accepted papers for CVPR (IEEE/CVF Conference on Computer Vision and Pattern Recognition), one dataset per year. • 14 items • Updated
paper_id uint32 0 2.07k | title stringlengths 20 137 | authors listlengths 1 85 | cvf_url stringlengths 98 187 | pdf_url stringlengths 99 188 | supp_url stringlengths 102 144 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 309 1.96k | abstract large_stringlengths 403 2.27k ⌀ | embedding listlengths 768 768 |
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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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