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 2.35k | title stringlengths 12 150 | authors listlengths 1 125 | cvf_url stringlengths 91 198 | pdf_url stringlengths 92 199 | supp_url stringlengths 101 171 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 302 2.79k | abstract large_stringlengths 394 2.05k | embedding listlengths 768 768 |
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0 | GFPose: Learning 3D Human Pose Prior With Gradient Fields | [
"Hai Ci",
"Mingdong Wu",
"Wentao Zhu",
"Xiaoxuan Ma",
"Hao Dong",
"Fangwei Zhong",
"Yizhou Wang"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ci_GFPose_Learning_3D_CVPR_2023_supplemental.pdf | 2212.08641 | cvf | @InProceedings{Ci_2023_CVPR,
author = {Ci, Hai and Wu, Mingdong and Zhu, Wentao and Ma, Xiaoxuan and Dong, Hao and Zhong, Fangwei and Wang, Yizhou},
title = {GFPose: Learning 3D Human Pose Prior With Gradient Fields},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the core of GFPose is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed 3D ... | [
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1 | CXTrack: Improving 3D Point Cloud Tracking With Contextual Information | [
"Tian-Xing Xu",
"Yuan-Chen Guo",
"Yu-Kun Lai",
"Song-Hai Zhang"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_CXTrack_Improving_3D_CVPR_2023_supplemental.pdf | 2211.08542 | cvf | @InProceedings{Xu_2023_CVPR,
author = {Xu, Tian-Xing and Guo, Yuan-Chen and Lai, Yu-Kun and Zhang, Song-Hai},
title = {CXTrack: Improving 3D Point Cloud Tracking With Contextual Information},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | 3D single object tracking plays an essential role in many applications, such as autonomous driving. It remains a challenging problem due to the large appearance variation and the sparsity of points caused by occlusion and limited sensor capabilities. Therefore, contextual information across two consecutive frames is cr... | [
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2 | Deep Frequency Filtering for Domain Generalization | [
"Shiqi Lin",
"Zhizheng Zhang",
"Zhipeng Huang",
"Yan Lu",
"Cuiling Lan",
"Peng Chu",
"Quanzeng You",
"Jiang Wang",
"Zicheng Liu",
"Amey Parulkar",
"Viraj Navkal",
"Zhibo Chen"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Deep_Frequency_Filtering_for_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Deep_Frequency_Filtering_for_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Deep_Frequency_Filtering_CVPR_2023_supplemental.pdf | 2203.12198 | cvf | @InProceedings{Lin_2023_CVPR,
author = {Lin, Shiqi and Zhang, Zhizheng and Huang, Zhipeng and Lu, Yan and Lan, Cuiling and Chu, Peng and You, Quanzeng and Wang, Jiang and Liu, Zicheng and Parulkar, Amey and Navkal, Viraj and Chen, Zhibo},
title = {Deep Frequency Filtering for Domain Generalization},
... | Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have uncovered that DNNs have preferences for some frequency components in the learning process and indicated that this may affect the robustness of... | [
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3 | Frame Flexible Network | [
"Yitian Zhang",
"Yue Bai",
"Chang Liu",
"Huan Wang",
"Sheng Li",
"Yun Fu"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Frame_Flexible_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Frame_Flexible_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Frame_Flexible_Network_CVPR_2023_supplemental.pdf | 2303.14817 | cvf | @InProceedings{Zhang_2023_CVPR,
author = {Zhang, Yitian and Bai, Yue and Liu, Chang and Wang, Huan and Li, Sheng and Fu, Yun},
title = {Frame Flexible Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Existing video recognition algorithms always conduct different training pipelines for inputs with different frame numbers, which requires repetitive training operations and multiplying storage costs. If we evaluate the model using other frames which are not used in training, we observe the performance will drop signifi... | [
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4 | Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow | [
"Hanyu Zhou",
"Yi Chang",
"Wending Yan",
"Luxin Yan"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Unsupervised_Cumulative_Domain_Adaptation_for_Foggy_Scene_Optical_Flow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Unsupervised_Cumulative_Domain_Adaptation_for_Foggy_Scene_Optical_Flow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Unsupervised_Cumulative_Domain_CVPR_2023_supplemental.zip | 2303.07564 | cvf | @InProceedings{Zhou_2023_CVPR,
author = {Zhou, Hanyu and Chang, Yi and Yan, Wending and Yan, Luxin},
title = {Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing methods typically adopt the domain adaptation to transfer the motion knowledge from clean to synthetic foggy domain. However, these methods unexpe... | [
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5 | NoisyTwins: Class-Consistent and Diverse Image Generation Through StyleGANs | [
"Harsh Rangwani",
"Lavish Bansal",
"Kartik Sharma",
"Tejan Karmali",
"Varun Jampani",
"R. Venkatesh Babu"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Rangwani_NoisyTwins_Class-Consistent_and_Diverse_Image_Generation_Through_StyleGANs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Rangwani_NoisyTwins_Class-Consistent_and_Diverse_Image_Generation_Through_StyleGANs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rangwani_NoisyTwins_Class-Consistent_and_CVPR_2023_supplemental.pdf | 2304.05866 | cvf | @InProceedings{Rangwani_2023_CVPR,
author = {Rangwani, Harsh and Bansal, Lavish and Sharma, Kartik and Karmali, Tejan and Jampani, Varun and Babu, R. Venkatesh},
title = {NoisyTwins: Class-Consistent and Diverse Image Generation Through StyleGANs},
booktitle = {Proceedings of the IEEE/CVF Conference ... | StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulation. However, the performance of StyleGANs severely degrades when trained via class-conditioning on large-scale long-tailed datasets. We fin... | [
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6 | DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-Aware Scene Synthesis | [
"Yinghao Xu",
"Menglei Chai",
"Zifan Shi",
"Sida Peng",
"Ivan Skorokhodov",
"Aliaksandr Siarohin",
"Ceyuan Yang",
"Yujun Shen",
"Hsin-Ying Lee",
"Bolei Zhou",
"Sergey Tulyakov"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_DisCoScene_Spatially_Disentangled_Generative_Radiance_Fields_for_Controllable_3D-Aware_Scene_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_DisCoScene_Spatially_Disentangled_Generative_Radiance_Fields_for_Controllable_3D-Aware_Scene_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_DisCoScene_Spatially_Disentangled_CVPR_2023_supplemental.pdf | 2212.11984 | cvf | @InProceedings{Xu_2023_CVPR,
author = {Xu, Yinghao and Chai, Menglei and Shi, Zifan and Peng, Sida and Skorokhodov, Ivan and Siarohin, Aliaksandr and Yang, Ceyuan and Shen, Yujun and Lee, Hsin-Ying and Zhou, Bolei and Tulyakov, Sergey},
title = {DisCoScene: Spatially Disentangled Generative Radiance Fiel... | Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects. This work presents DisCoScene: a 3D-aware generative model for high-quality and controllable scene synthesis. The key ingredient of ou... | [
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7 | Revisiting Self-Similarity: Structural Embedding for Image Retrieval | [
"Seongwon Lee",
"Suhyeon Lee",
"Hongje Seong",
"Euntai Kim"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Revisiting_Self-Similarity_Structural_Embedding_for_Image_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Revisiting_Self-Similarity_Structural_Embedding_for_Image_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Revisiting_Self-Similarity_Structural_CVPR_2023_supplemental.pdf | null | null | @InProceedings{Lee_2023_CVPR,
author = {Lee, Seongwon and Lee, Suhyeon and Seong, Hongje and Kim, Euntai},
title = {Revisiting Self-Similarity: Structural Embedding for Image Retrieval},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Despite advances in global image representation, existing image retrieval approaches rarely consider geometric structure during the global retrieval stage. In this work, we revisit the conventional self-similarity descriptor from a convolutional perspective, to encode both the visual and structural cues of the image to... | [
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8 | Minimizing the Accumulated Trajectory Error To Improve Dataset Distillation | [
"Jiawei Du",
"Yidi Jiang",
"Vincent Y. F. Tan",
"Joey Tianyi Zhou",
"Haizhou Li"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Minimizing_the_Accumulated_Trajectory_Error_To_Improve_Dataset_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Minimizing_the_Accumulated_Trajectory_Error_To_Improve_Dataset_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Minimizing_the_Accumulated_CVPR_2023_supplemental.pdf | 2211.11004 | cvf | @InProceedings{Du_2023_CVPR,
author = {Du, Jiawei and Jiang, Yidi and Tan, Vincent Y. F. and Zhou, Joey Tianyi and Li, Haizhou},
title = {Minimizing the Accumulated Trajectory Error To Improve Dataset Distillation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | Model-based deep learning has achieved astounding successes due in part to the availability of large-scale real-world data. However, processing such massive amounts of data comes at a considerable cost in terms of computations, storage, training and the search for good neural architectures. Dataset distillation has thu... | [
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9 | Decoupling-and-Aggregating for Image Exposure Correction | [
"Yang Wang",
"Long Peng",
"Liang Li",
"Yang Cao",
"Zheng-Jun Zha"
] | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Decoupling-and-Aggregating_for_Image_Exposure_Correction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Decoupling-and-Aggregating_for_Image_Exposure_Correction_CVPR_2023_paper.pdf | null | null | null | @InProceedings{Wang_2023_CVPR,
author = {Wang, Yang and Peng, Long and Li, Liang and Cao, Yang and Zha, Zheng-Jun},
title = {Decoupling-and-Aggregating for Image Exposure Correction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | The images captured under improper exposure conditions often suffer from contrast degradation and detail distortion. Contrast degradation will destroy the statistical properties of low-frequency components, while detail distortion will disturb the structural properties of high-frequency components, leading to the low-f... | [
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