paper_id stringlengths 33 138 | title stringlengths 13 147 | authors listlengths 1 17 | cvf_url stringlengths 90 195 | pdf_url stringlengths 91 196 | supp_url stringlengths 101 137 ⌀ | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 3
values | bibtex large_stringlengths 305 619 | abstract large_stringlengths 449 1.99k |
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Vesdapunt_CRFace_Confidence_Ranker_for_Model-Agnostic_Face_Detection_Refinement_CVPR_2021_paper | CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement | [
"Noranart Vesdapunt",
"Baoyuan Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Vesdapunt_CRFace_Confidence_Ranker_for_Model-Agnostic_Face_Detection_Refinement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Vesdapunt_CRFace_Confidence_Ranker_for_Model-Agnostic_Face_Detection_Refinement_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Vesdapunt_CRFace_Confidence_Ranker_CVPR_2021_supplemental.pdf | 2103.07017 | cvf | @InProceedings{Vesdapunt_2021_CVPR,
author = {Vesdapunt, Noranart and Wang, Baoyuan},
title = {CRFace: Confidence Ranker for Model-Agnostic Face Detection Refinement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
y... | Face detection is a fundamental problem for many downstream face applications, and there is a rising demand for faster, more accurate yet support for higher resolution face detectors. Recent smartphones can record a video in 8K resolution, but many of the existing face detectors still fail due to the anchor size and tr... |
Chen_Semantic_Audio-Visual_Navigation_CVPR_2021_paper | Semantic Audio-Visual Navigation | [
"Changan Chen",
"Ziad Al-Halah",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Semantic_Audio-Visual_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Semantic_Audio-Visual_Navigation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Semantic_Audio-Visual_Navigation_CVPR_2021_supplemental.zip | 2012.11583 | title_snapshot | @InProceedings{Chen_2021_CVPR,
author = {Chen, Changan and Al-Halah, Ziad and Grauman, Kristen},
title = {Semantic Audio-Visual Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
page... | Recent work on audio-visual navigation assumes a constantly-sounding target and restricts the role of audio to signaling the target's position. We introduce semantic audio-visual navigation, where objects in the environment make sounds consistent with their semantic meaning (e.g., toilet flushing, door creaking) and ac... |
Tang_Humble_Teachers_Teach_Better_Students_for_Semi-Supervised_Object_Detection_CVPR_2021_paper | Humble Teachers Teach Better Students for Semi-Supervised Object Detection | [
"Yihe Tang",
"Weifeng Chen",
"Yijun Luo",
"Yuting Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Tang_Humble_Teachers_Teach_Better_Students_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Tang_Humble_Teachers_Teach_Better_Students_for_Semi-Supervised_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Tang_Humble_Teachers_Teach_CVPR_2021_supplemental.pdf | 2106.10456 | cvf | @InProceedings{Tang_2021_CVPR,
author = {Tang, Yihe and Chen, Weifeng and Luo, Yijun and Zhang, Yuting},
title = {Humble Teachers Teach Better Students for Semi-Supervised Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework. Our method is featured with 1) the exponential moving averaging strategy to update the teacher from the student online, 2) using plenty of region proposals and soft pseudo-labels as the student's ... |
Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper | One Shot Face Swapping on Megapixels | [
"Yuhao Zhu",
"Qi Li",
"Jian Wang",
"Cheng-Zhong Xu",
"Zhenan Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_One_Shot_Face_Swapping_on_Megapixels_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_One_Shot_Face_CVPR_2021_supplemental.pdf | 2105.04932 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Yuhao and Li, Qi and Wang, Jian and Xu, Cheng-Zhong and Sun, Zhenan},
title = {One Shot Face Swapping on Megapixels},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Face swapping has both positive applications such as entertainment, human-computer interaction, etc., and negative applications such as DeepFake threats to politics, economics, etc. Nevertheless, it is necessary to understand the scheme of advanced methods for high-quality face swapping and generate enough and represen... |
Ding_CDFI_Compression-Driven_Network_Design_for_Frame_Interpolation_CVPR_2021_paper | CDFI: Compression-Driven Network Design for Frame Interpolation | [
"Tianyu Ding",
"Luming Liang",
"Zhihui Zhu",
"Ilya Zharkov"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ding_CDFI_Compression-Driven_Network_Design_for_Frame_Interpolation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ding_CDFI_Compression-Driven_Network_Design_for_Frame_Interpolation_CVPR_2021_paper.pdf | null | 2103.10559 | cvf | @InProceedings{Ding_2021_CVPR,
author = {Ding, Tianyu and Liang, Luming and Zhu, Zhihui and Zharkov, Ilya},
title = {CDFI: Compression-Driven Network Design for Frame Interpolation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design fo... |
Xu_PAConv_Position_Adaptive_Convolution_With_Dynamic_Kernel_Assembling_on_Point_CVPR_2021_paper | PAConv: Position Adaptive Convolution With Dynamic Kernel Assembling on Point Clouds | [
"Mutian Xu",
"Runyu Ding",
"Hengshuang Zhao",
"Xiaojuan Qi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xu_PAConv_Position_Adaptive_Convolution_With_Dynamic_Kernel_Assembling_on_Point_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xu_PAConv_Position_Adaptive_Convolution_With_Dynamic_Kernel_Assembling_on_Point_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xu_PAConv_Position_Adaptive_CVPR_2021_supplemental.pdf | 2103.14635 | cvf | @InProceedings{Xu_2021_CVPR,
author = {Xu, Mutian and Ding, Runyu and Zhao, Hengshuang and Qi, Xiaojuan},
title = {PAConv: Position Adaptive Convolution With Dynamic Kernel Assembling on Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | We introduce Position Adaptive Convolution (PAConv), a generic convolution operation for 3D point cloud processing. The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned ... |
Wang_End-to-End_Object_Detection_With_Fully_Convolutional_Network_CVPR_2021_paper | End-to-End Object Detection With Fully Convolutional Network | [
"Jianfeng Wang",
"Lin Song",
"Zeming Li",
"Hongbin Sun",
"Jian Sun",
"Nanning Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_End-to-End_Object_Detection_With_Fully_Convolutional_Network_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_End-to-End_Object_Detection_With_Fully_Convolutional_Network_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_End-to-End_Object_Detection_CVPR_2021_supplemental.pdf | 2012.03544 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Jianfeng and Song, Lin and Li, Zeming and Sun, Hongbin and Sun, Jian and Zheng, Nanning},
title = {End-to-End Object Detection With Fully Convolutional Network},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS) post-processing, which impedes fully end-to-end training. In this paper, we give the analysis of discarding NMS, where the results reveal ... |
Barath_Efficient_Initial_Pose-Graph_Generation_for_Global_SfM_CVPR_2021_paper | Efficient Initial Pose-Graph Generation for Global SfM | [
"Daniel Barath",
"Dmytro Mishkin",
"Ivan Eichhardt",
"Ilia Shipachev",
"Jiri Matas"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Barath_Efficient_Initial_Pose-Graph_Generation_for_Global_SfM_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Barath_Efficient_Initial_Pose-Graph_Generation_for_Global_SfM_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Barath_Efficient_Initial_Pose-Graph_CVPR_2021_supplemental.pdf | 2011.11986 | cvf | @InProceedings{Barath_2021_CVPR,
author = {Barath, Daniel and Mishkin, Dmytro and Eichhardt, Ivan and Shipachev, Ilia and Matas, Jiri},
title = {Efficient Initial Pose-Graph Generation for Global SfM},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVP... | We propose ways to speed up the initial pose-graph generation for global Structure-from-Motion algorithms. To avoid forming tentative point correspondences by FLANN and geometric verification by RANSAC, which are the most time-consuming steps of the pose-graph creation, we propose two new methods -- built on the fact t... |
Gao_Representative_Batch_Normalization_With_Feature_Calibration_CVPR_2021_paper | Representative Batch Normalization With Feature Calibration | [
"Shang-Hua Gao",
"Qi Han",
"Duo Li",
"Ming-Ming Cheng",
"Pai Peng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gao_Representative_Batch_Normalization_With_Feature_Calibration_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gao_Representative_Batch_Normalization_With_Feature_Calibration_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Gao_2021_CVPR,
author = {Gao, Shang-Hua and Han, Qi and Li, Duo and Cheng, Ming-Ming and Peng, Pai},
title = {Representative Batch Normalization With Feature Calibration},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Batch Normalization (BatchNorm) has become the default component in modern neural networks to stabilize training. In BatchNorm, centering and scaling operations, along with mean and variance statistics, are utilized for feature standardization over the batch dimension. The batch dependency of BatchNorm enables stable t... |
Zhang_VarifocalNet_An_IoU-Aware_Dense_Object_Detector_CVPR_2021_paper | VarifocalNet: An IoU-Aware Dense Object Detector | [
"Haoyang Zhang",
"Ying Wang",
"Feras Dayoub",
"Niko Sunderhauf"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_VarifocalNet_An_IoU-Aware_Dense_Object_Detector_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_VarifocalNet_An_IoU-Aware_Dense_Object_Detector_CVPR_2021_paper.pdf | null | 2008.13367 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Haoyang and Wang, Ying and Dayoub, Feras and Sunderhauf, Niko},
title = {VarifocalNet: An IoU-Aware Dense Object Detector},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degr... |
Oh_Background-Aware_Pooling_and_Noise-Aware_Loss_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2021_paper | Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation | [
"Youngmin Oh",
"Beomjun Kim",
"Bumsub Ham"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Oh_Background-Aware_Pooling_and_Noise-Aware_Loss_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Oh_Background-Aware_Pooling_and_Noise-Aware_Loss_for_Weakly-Supervised_Semantic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Oh_Background-Aware_Pooling_and_CVPR_2021_supplemental.pdf | 2104.00905 | cvf | @InProceedings{Oh_2021_CVPR,
author = {Oh, Youngmin and Kim, Beomjun and Ham, Bumsub},
title = {Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | We address the problem of weakly-supervised semantic segmentation (WSSS) using bounding box annotations. Although object bounding boxes are good indicators to segment corresponding objects, they do not specify object boundaries, making it hard to train convolutional neural networks (CNNs) for semantic segmentation. We ... |
Zhang_Abstract_Spatial-Temporal_Reasoning_via_Probabilistic_Abduction_and_Execution_CVPR_2021_paper | Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution | [
"Chi Zhang",
"Baoxiong Jia",
"Song-Chun Zhu",
"Yixin Zhu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Abstract_Spatial-Temporal_Reasoning_via_Probabilistic_Abduction_and_Execution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Abstract_Spatial-Temporal_Reasoning_via_Probabilistic_Abduction_and_Execution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Abstract_Spatial-Temporal_Reasoning_CVPR_2021_supplemental.pdf | 2103.14230 | cvf | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Chi and Jia, Baoxiong and Zhu, Song-Chun and Zhu, Yixin},
title = {Abstract Spatial-Temporal Reasoning via Probabilistic Abduction and Execution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Spatial-temporal reasoning is a challenging task in Artificial Intelligence (AI) due to its demanding but unique nature: a theoretic requirement on representing and reasoning based on spatial-temporal knowledge in mind, and an applied requirement on a high-level cognitive system capable of navigating and acting in spac... |
Nam_Reducing_Domain_Gap_by_Reducing_Style_Bias_CVPR_2021_paper | Reducing Domain Gap by Reducing Style Bias | [
"Hyeonseob Nam",
"HyunJae Lee",
"Jongchan Park",
"Wonjun Yoon",
"Donggeun Yoo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Nam_Reducing_Domain_Gap_by_Reducing_Style_Bias_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Nam_Reducing_Domain_Gap_by_Reducing_Style_Bias_CVPR_2021_paper.pdf | null | 1910.11645 | cvf | @InProceedings{Nam_2021_CVPR,
author = {Nam, Hyeonseob and Lee, HyunJae and Park, Jongchan and Yoon, Wonjun and Yoo, Donggeun},
title = {Reducing Domain Gap by Reducing Style Bias},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month =... | Convolutional Neural Networks (CNNs) often fail to maintain their performance when they confront new test domains, which is known as the problem of domain shift. Recent studies suggest that one of the main causes of this problem is CNNs' strong inductive bias towards image styles (i.e. textures) which are sensitive to ... |
Xie_Efficient_Regional_Memory_Network_for_Video_Object_Segmentation_CVPR_2021_paper | Efficient Regional Memory Network for Video Object Segmentation | [
"Haozhe Xie",
"Hongxun Yao",
"Shangchen Zhou",
"Shengping Zhang",
"Wenxiu Sun"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xie_Efficient_Regional_Memory_Network_for_Video_Object_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Efficient_Regional_Memory_Network_for_Video_Object_Segmentation_CVPR_2021_paper.pdf | null | 2103.12934 | cvf | @InProceedings{Xie_2021_CVPR,
author = {Xie, Haozhe and Yao, Hongxun and Zhou, Shangchen and Zhang, Shengping and Sun, Wenxiu},
title = {Efficient Regional Memory Network for Video Object Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CV... | Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. However, these methods exploit the information from the memory by global-to-global matching between th... |
Guzov_Human_POSEitioning_System_HPS_3D_Human_Pose_Estimation_and_Self-Localization_CVPR_2021_paper | Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-Localization in Large Scenes From Body-Mounted Sensors | [
"Vladimir Guzov",
"Aymen Mir",
"Torsten Sattler",
"Gerard Pons-Moll"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guzov_Human_POSEitioning_System_HPS_3D_Human_Pose_Estimation_and_Self-Localization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guzov_Human_POSEitioning_System_HPS_3D_Human_Pose_Estimation_and_Self-Localization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Guzov_Human_POSEitioning_System_CVPR_2021_supplemental.pdf | 2103.17265 | cvf | @InProceedings{Guzov_2021_CVPR,
author = {Guzov, Vladimir and Mir, Aymen and Sattler, Torsten and Pons-Moll, Gerard},
title = {Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-Localization in Large Scenes From Body-Mounted Sensors},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | We introduce (HPS) Human POSEitioning System, a method to recover the full 3D pose of a human registered with a 3D scan of the surrounding environment using wearable sensors. Using IMUs attached at the body limbs and a head mounted camera looking outwards, HPS fuses camera based self-localization with IMU-based human b... |
Zhu_Semantic_Relation_Reasoning_for_Shot-Stable_Few-Shot_Object_Detection_CVPR_2021_paper | Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection | [
"Chenchen Zhu",
"Fangyi Chen",
"Uzair Ahmed",
"Zhiqiang Shen",
"Marios Savvides"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Semantic_Relation_Reasoning_for_Shot-Stable_Few-Shot_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Semantic_Relation_Reasoning_for_Shot-Stable_Few-Shot_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhu_Semantic_Relation_Reasoning_CVPR_2021_supplemental.pdf | 2103.01903 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Chenchen and Chen, Fangyi and Ahmed, Uzair and Shen, Zhiqiang and Savvides, Marios},
title = {Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni... | Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data scarcity of novel classes. But the semantic relation between the novel classes and the base classes is constant regardless of the data availa... |
Guo_Online_Multiple_Object_Tracking_With_Cross-Task_Synergy_CVPR_2021_paper | Online Multiple Object Tracking With Cross-Task Synergy | [
"Song Guo",
"Jingya Wang",
"Xinchao Wang",
"Dacheng Tao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Guo_Online_Multiple_Object_Tracking_With_Cross-Task_Synergy_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Online_Multiple_Object_Tracking_With_Cross-Task_Synergy_CVPR_2021_paper.pdf | null | 2104.00380 | cvf | @InProceedings{Guo_2021_CVPR,
author = {Guo, Song and Wang, Jingya and Wang, Xinchao and Tao, Dacheng},
title = {Online Multiple Object Tracking With Cross-Task Synergy},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Modern online multiple object tracking (MOT) methods usually focus on two directions to improve tracking performance. One is to predict new positions in an incoming frame based on tracking information from previous frames, and the other is to enhance data association by generating more discriminative identity embedding... |
Neverova_Discovering_Relationships_Between_Object_Categories_via_Universal_Canonical_Maps_CVPR_2021_paper | Discovering Relationships Between Object Categories via Universal Canonical Maps | [
"Natalia Neverova",
"Artsiom Sanakoyeu",
"Patrick Labatut",
"David Novotny",
"Andrea Vedaldi"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Neverova_Discovering_Relationships_Between_Object_Categories_via_Universal_Canonical_Maps_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Neverova_Discovering_Relationships_Between_Object_Categories_via_Universal_Canonical_Maps_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Neverova_Discovering_Relationships_Between_CVPR_2021_supplemental.pdf | 2106.09758 | cvf | @InProceedings{Neverova_2021_CVPR,
author = {Neverova, Natalia and Sanakoyeu, Artsiom and Labatut, Patrick and Novotny, David and Vedaldi, Andrea},
title = {Discovering Relationships Between Object Categories via Universal Canonical Maps},
booktitle = {Proceedings of the IEEE/CVF Conference on Comput... | We tackle the problem of learning the geometry of multiple categories of deformable objects jointly. Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects. However, training such models requires to initialize inter-category correspondences by hand. T... |
Zhao_Prior_Based_Human_Completion_CVPR_2021_paper | Prior Based Human Completion | [
"Zibo Zhao",
"Wen Liu",
"Yanyu Xu",
"Xianing Chen",
"Weixin Luo",
"Lei Jin",
"Bohui Zhu",
"Tong Liu",
"Binqiang Zhao",
"Shenghua Gao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Prior_Based_Human_Completion_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Prior_Based_Human_Completion_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Prior_Based_Human_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Zibo and Liu, Wen and Xu, Yanyu and Chen, Xianing and Luo, Weixin and Jin, Lei and Zhu, Bohui and Liu, Tong and Zhao, Binqiang and Gao, Shenghua},
title = {Prior Based Human Completion},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vis... | We study a very challenging task, human image completion, which tries to recover the human body part with a reasonable human shape from the corrupted region. Since each human body part is unique, it is infeasible to restore the missing part by borrowing textures from other visible regions. Thus, we propose two types of... |
Khakzar_Neural_Response_Interpretation_Through_the_Lens_of_Critical_Pathways_CVPR_2021_paper | Neural Response Interpretation Through the Lens of Critical Pathways | [
"Ashkan Khakzar",
"Soroosh Baselizadeh",
"Saurabh Khanduja",
"Christian Rupprecht",
"Seong Tae Kim",
"Nassir Navab"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Khakzar_Neural_Response_Interpretation_Through_the_Lens_of_Critical_Pathways_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Khakzar_Neural_Response_Interpretation_Through_the_Lens_of_Critical_Pathways_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Khakzar_Neural_Response_Interpretation_CVPR_2021_supplemental.pdf | 2103.16886 | cvf | @InProceedings{Khakzar_2021_CVPR,
author = {Khakzar, Ashkan and Baselizadeh, Soroosh and Khanduja, Saurabh and Rupprecht, Christian and Kim, Seong Tae and Navab, Nassir},
title = {Neural Response Interpretation Through the Lens of Critical Pathways},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequently leverage them for interpreting the network's response to an input. The pruning objective --- selecting the smallest group of neurons f... |
Wang_Rethinking_and_Improving_the_Robustness_of_Image_Style_Transfer_CVPR_2021_paper | Rethinking and Improving the Robustness of Image Style Transfer | [
"Pei Wang",
"Yijun Li",
"Nuno Vasconcelos"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Rethinking_and_Improving_the_Robustness_of_Image_Style_Transfer_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Rethinking_and_Improving_the_Robustness_of_Image_Style_Transfer_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Rethinking_and_Improving_CVPR_2021_supplemental.pdf | 2104.05623 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Pei and Li, Yijun and Vasconcelos, Nuno},
title = {Rethinking and Improving the Robustness of Image Style Transfer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has remarkable ability to capture the visual style of an image. Surprisingly, however, this stylization quality is not robust and often degrades significantly when applied to featur... |
Sun_FSCE_Few-Shot_Object_Detection_via_Contrastive_Proposal_Encoding_CVPR_2021_paper | FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding | [
"Bo Sun",
"Banghuai Li",
"Shengcai Cai",
"Ye Yuan",
"Chi Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_FSCE_Few-Shot_Object_Detection_via_Contrastive_Proposal_Encoding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_FSCE_Few-Shot_Object_Detection_via_Contrastive_Proposal_Encoding_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_FSCE_Few-Shot_Object_CVPR_2021_supplemental.pdf | 2103.05950 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Bo and Li, Banghuai and Cai, Shengcai and Yuan, Ye and Zhang, Chi},
title = {FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | Emerging interests have been brought to recognize previously unseen objects given very few training examples, known as few-shot object detection (FSOD). Recent researches demonstrate that good feature embedding is the key to reach favorable few-shot learning performance. We observe object proposals with different Inter... |
Faraki_Cross-Domain_Similarity_Learning_for_Face_Recognition_in_Unseen_Domains_CVPR_2021_paper | Cross-Domain Similarity Learning for Face Recognition in Unseen Domains | [
"Masoud Faraki",
"Xiang Yu",
"Yi-Hsuan Tsai",
"Yumin Suh",
"Manmohan Chandraker"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Faraki_Cross-Domain_Similarity_Learning_for_Face_Recognition_in_Unseen_Domains_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Faraki_Cross-Domain_Similarity_Learning_for_Face_Recognition_in_Unseen_Domains_CVPR_2021_paper.pdf | null | 2103.07503 | cvf | @InProceedings{Faraki_2021_CVPR,
author = {Faraki, Masoud and Yu, Xiang and Tsai, Yi-Hsuan and Suh, Yumin and Chandraker, Manmohan},
title = {Cross-Domain Similarity Learning for Face Recognition in Unseen Domains},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Re... | Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as a novel ethnicity or unpredictable individual make-ups during test time. In this paper, we introduce a novel cross-domain metric learning lo... |
Chen_Learning_3D_Shape_Feature_for_Texture-Insensitive_Person_Re-Identification_CVPR_2021_paper | Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification | [
"Jiaxing Chen",
"Xinyang Jiang",
"Fudong Wang",
"Jun Zhang",
"Feng Zheng",
"Xing Sun",
"Wei-Shi Zheng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Learning_3D_Shape_Feature_for_Texture-Insensitive_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_3D_Shape_Feature_for_Texture-Insensitive_Person_Re-Identification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Learning_3D_Shape_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Jiaxing and Jiang, Xinyang and Wang, Fudong and Zhang, Jun and Zheng, Feng and Sun, Xing and Zheng, Wei-Shi},
title = {Learning 3D Shape Feature for Texture-Insensitive Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Com... | It is well acknowledged that person re-identification (person ReID) highly relies on visual texture information like clothing. Despite significant progress has been made in recent years, texture-confusing situations like clothing changing and persons wearing the same clothes receive little attention from most existing ... |
Li_Virtual_Fully-Connected_Layer_Training_a_Large-Scale_Face_Recognition_Dataset_With_CVPR_2021_paper | Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset With Limited Computational Resources | [
"Pengyu Li",
"Biao Wang",
"Lei Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Virtual_Fully-Connected_Layer_Training_a_Large-Scale_Face_Recognition_Dataset_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Virtual_Fully-Connected_Layer_Training_a_Large-Scale_Face_Recognition_Dataset_With_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Virtual_Fully-Connected_Layer_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Pengyu and Wang, Biao and Zhang, Lei},
title = {Virtual Fully-Connected Layer: Training a Large-Scale Face Recognition Dataset With Limited Computational Resources},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Recently, deep face recognition has achieved significant progress because of Convolutional Neural Networks (CNNs) and large-scale datasets. However, training CNNs on a large-scale face recognition dataset with limited computational resources is still a challenge. This is because the classification paradigm needs to tra... |
Mustafa_Multi-Person_Implicit_Reconstruction_From_a_Single_Image_CVPR_2021_paper | Multi-Person Implicit Reconstruction From a Single Image | [
"Armin Mustafa",
"Akin Caliskan",
"Lourdes Agapito",
"Adrian Hilton"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mustafa_Multi-Person_Implicit_Reconstruction_From_a_Single_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mustafa_Multi-Person_Implicit_Reconstruction_From_a_Single_Image_CVPR_2021_paper.pdf | null | 2104.09283 | cvf | @InProceedings{Mustafa_2021_CVPR,
author = {Mustafa, Armin and Caliskan, Akin and Agapito, Lourdes and Hilton, Adrian},
title = {Multi-Person Implicit Reconstruction From a Single Image},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month... | We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image. Existing multi-person methods suffer from two main drawbacks: they are often model-based and therefore cannot capture accurate 3D models of people with loose clothing and hair... |
Liang_OPANAS_One-Shot_Path_Aggregation_Network_Architecture_Search_for_Object_Detection_CVPR_2021_paper | OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection | [
"Tingting Liang",
"Yongtao Wang",
"Zhi Tang",
"Guosheng Hu",
"Haibin Ling"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_OPANAS_One-Shot_Path_Aggregation_Network_Architecture_Search_for_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_OPANAS_One-Shot_Path_Aggregation_Network_Architecture_Search_for_Object_Detection_CVPR_2021_paper.pdf | null | 2103.04507 | cvf | @InProceedings{Liang_2021_CVPR,
author = {Liang, Tingting and Wang, Yongtao and Tang, Zhi and Hu, Guosheng and Ling, Haibin},
title = {OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Recently, neural architecture search (NAS) has been exploited to design feature pyramid networks (FPNs) and achieved promising results for visual object detection. Encouraged by the success, we propose a novel One-Shot Path Aggregation Network Architecture Search (OPANAS) algorithm, which significantly improves both se... |
Park_Bridge_To_Answer_Structure-Aware_Graph_Interaction_Network_for_Video_Question_CVPR_2021_paper | Bridge To Answer: Structure-Aware Graph Interaction Network for Video Question Answering | [
"Jungin Park",
"Jiyoung Lee",
"Kwanghoon Sohn"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Park_Bridge_To_Answer_Structure-Aware_Graph_Interaction_Network_for_Video_Question_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Park_Bridge_To_Answer_Structure-Aware_Graph_Interaction_Network_for_Video_Question_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Park_Bridge_To_Answer_CVPR_2021_supplemental.pdf | 2104.14085 | cvf | @InProceedings{Park_2021_CVPR,
author = {Park, Jungin and Lee, Jiyoung and Sohn, Kwanghoon},
title = {Bridge To Answer: Structure-Aware Graph Interaction Network for Video Question Answering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | This paper presents a novel method, termed Bridge to Answer, to infer correct answers for questions about a given video by leveraging adequate graph interactions of heterogeneous crossmodal graphs. To realize this, we learn question conditioned visual graphs by exploiting the relation between video and question to enab... |
Wang_Learning_Compositional_Radiance_Fields_of_Dynamic_Human_Heads_CVPR_2021_paper | Learning Compositional Radiance Fields of Dynamic Human Heads | [
"Ziyan Wang",
"Timur Bagautdinov",
"Stephen Lombardi",
"Tomas Simon",
"Jason Saragih",
"Jessica Hodgins",
"Michael Zollhofer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Learning_Compositional_Radiance_Fields_of_Dynamic_Human_Heads_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Learning_Compositional_Radiance_Fields_of_Dynamic_Human_Heads_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wang_Learning_Compositional_Radiance_CVPR_2021_supplemental.pdf | 2012.09955 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Ziyan and Bagautdinov, Timur and Lombardi, Stephen and Simon, Tomas and Saragih, Jason and Hodgins, Jessica and Zollhofer, Michael},
title = {Learning Compositional Radiance Fields of Dynamic Human Heads},
booktitle = {Proceedings of the IEEE/CVF Confere... | Photorealistic rendering of dynamic humans is an important ability for telepresence systems, virtual shopping, synthetic data generation, and more. Recently, neural rendering methods, which combine techniques from computer graphics and machine learning, have created high-fidelity models of humans and objects. Some of t... |
He_Partial_Person_Re-Identification_With_Part-Part_Correspondence_Learning_CVPR_2021_paper | Partial Person Re-Identification With Part-Part Correspondence Learning | [
"Tianyu He",
"Xu Shen",
"Jianqiang Huang",
"Zhibo Chen",
"Xian-Sheng Hua"
] | https://openaccess.thecvf.com/content/CVPR2021/html/He_Partial_Person_Re-Identification_With_Part-Part_Correspondence_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/He_Partial_Person_Re-Identification_With_Part-Part_Correspondence_Learning_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/He_Partial_Person_Re-Identification_CVPR_2021_supplemental.pdf | null | null | @InProceedings{He_2021_CVPR,
author = {He, Tianyu and Shen, Xu and Huang, Jianqiang and Chen, Zhibo and Hua, Xian-Sheng},
title = {Partial Person Re-Identification With Part-Part Correspondence Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID). Nonetheless, most of approaches assume that the input is given with the fulfillment of expectations, while imperfect input remains rarely explored to date, which is a non-trivial problem since directly a... |
Hampali_Monte_Carlo_Scene_Search_for_3D_Scene_Understanding_CVPR_2021_paper | Monte Carlo Scene Search for 3D Scene Understanding | [
"Shreyas Hampali",
"Sinisa Stekovic",
"Sayan Deb Sarkar",
"Chetan S. Kumar",
"Friedrich Fraundorfer",
"Vincent Lepetit"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hampali_Monte_Carlo_Scene_Search_for_3D_Scene_Understanding_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hampali_Monte_Carlo_Scene_Search_for_3D_Scene_Understanding_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Hampali_Monte_Carlo_Scene_CVPR_2021_supplemental.pdf | 2103.07969 | cvf | @InProceedings{Hampali_2021_CVPR,
author = {Hampali, Shreyas and Stekovic, Sinisa and Sarkar, Sayan Deb and Kumar, Chetan S. and Fraundorfer, Friedrich and Lepetit, Vincent},
title = {Monte Carlo Scene Search for 3D Scene Understanding},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer... | We explore how a general AI algorithm can be used for 3D scene understanding to reduce the need for training data. More exactly, we propose a modification of the Monte Carlo Tree Search (MCTS) algorithm to retrieve objects and room layouts from noisy RGB-D scans. While MCTS was developed as a game-playing algorithm, we... |
Zhang_Coarse-To-Fine_Person_Re-Identification_With_Auxiliary-Domain_Classification_and_Second-Order_Information_Bottleneck_CVPR_2021_paper | Coarse-To-Fine Person Re-Identification With Auxiliary-Domain Classification and Second-Order Information Bottleneck | [
"Anguo Zhang",
"Yueming Gao",
"Yuzhen Niu",
"Wenxi Liu",
"Yongcheng Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhang_Coarse-To-Fine_Person_Re-Identification_With_Auxiliary-Domain_Classification_and_Second-Order_Information_Bottleneck_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Coarse-To-Fine_Person_Re-Identification_With_Auxiliary-Domain_Classification_and_Second-Order_Information_Bottleneck_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhang_Coarse-To-Fine_Person_Re-Identification_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Zhang_2021_CVPR,
author = {Zhang, Anguo and Gao, Yueming and Niu, Yuzhen and Liu, Wenxi and Zhou, Yongcheng},
title = {Coarse-To-Fine Person Re-Identification With Auxiliary-Domain Classification and Second-Order Information Bottleneck},
booktitle = {Proceedings of the IEEE/CVF Confere... | Person re-identification (Re-ID) is to retrieve a particular person captured by different cameras, which is of great significance for security surveillance and pedestrian behavior analysis. However, due to the large intra-class variation of a person across cameras, e.g., occlusions, illuminations, viewpoints, and poses... |
Chen_Transformer_Tracking_CVPR_2021_paper | Transformer Tracking | [
"Xin Chen",
"Bin Yan",
"Jiawen Zhu",
"Dong Wang",
"Xiaoyun Yang",
"Huchuan Lu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Transformer_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Transformer_Tracking_CVPR_2021_paper.pdf | null | 2103.15436 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Xin and Yan, Bin and Zhu, Jiawen and Wang, Dong and Yang, Xiaoyun and Lu, Huchuan},
title = {Transformer Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Correlation acts as a critical role in the tracking field, especially in recent popular Siamese-based trackers. The correlation operation is a simple fusion manner to consider the similarity between the template and the search region. However, the correlation operation itself is a local linear matching process, leading... |
Wang_Structured_Multi-Level_Interaction_Network_for_Video_Moment_Localization_via_Language_CVPR_2021_paper | Structured Multi-Level Interaction Network for Video Moment Localization via Language Query | [
"Hao Wang",
"Zheng-Jun Zha",
"Liang Li",
"Dong Liu",
"Jiebo Luo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Structured_Multi-Level_Interaction_Network_for_Video_Moment_Localization_via_Language_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Structured_Multi-Level_Interaction_Network_for_Video_Moment_Localization_via_Language_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Hao and Zha, Zheng-Jun and Li, Liang and Liu, Dong and Luo, Jiebo},
title = {Structured Multi-Level Interaction Network for Video Moment Localization via Language Query},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern ... | We address the problem of localizing a specific moment described by a natural language query. Existing works interact the query with either video frame or moment proposal, and neglect the inherent structure of moment construction for both cross-modal understanding and video content comprehension, which are the two cruc... |
Wang_Structured_Scene_Memory_for_Vision-Language_Navigation_CVPR_2021_paper | Structured Scene Memory for Vision-Language Navigation | [
"Hanqing Wang",
"Wenguan Wang",
"Wei Liang",
"Caiming Xiong",
"Jianbing Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Structured_Scene_Memory_for_Vision-Language_Navigation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Structured_Scene_Memory_for_Vision-Language_Navigation_CVPR_2021_paper.pdf | null | 2103.03454 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Hanqing and Wang, Wenguan and Liang, Wei and Xiong, Caiming and Shen, Jianbing},
title = {Structured Scene Memory for Vision-Language Navigation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks... |
Fu_Unsupervised_Pre-Training_for_Person_Re-Identification_CVPR_2021_paper | Unsupervised Pre-Training for Person Re-Identification | [
"Dengpan Fu",
"Dongdong Chen",
"Jianmin Bao",
"Hao Yang",
"Lu Yuan",
"Lei Zhang",
"Houqiang Li",
"Dong Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Unsupervised_Pre-Training_for_Person_Re-Identification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Unsupervised_Pre-Training_for_Person_Re-Identification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Fu_Unsupervised_Pre-Training_for_CVPR_2021_supplemental.pdf | 2012.03753 | cvf | @InProceedings{Fu_2021_CVPR,
author = {Fu, Dengpan and Chen, Dongdong and Bao, Jianmin and Yang, Hao and Yuan, Lu and Zhang, Lei and Li, Houqiang and Chen, Dong},
title = {Unsupervised Pre-Training for Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision a... | In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation. This is to address the problem that all existing person Re... |
Li_Progressive_Stage-Wise_Learning_for_Unsupervised_Feature_Representation_Enhancement_CVPR_2021_paper | Progressive Stage-Wise Learning for Unsupervised Feature Representation Enhancement | [
"Zefan Li",
"Chenxi Liu",
"Alan Yuille",
"Bingbing Ni",
"Wenjun Zhang",
"Wen Gao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Progressive_Stage-Wise_Learning_for_Unsupervised_Feature_Representation_Enhancement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Progressive_Stage-Wise_Learning_for_Unsupervised_Feature_Representation_Enhancement_CVPR_2021_paper.pdf | null | 2106.05554 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Zefan and Liu, Chenxi and Yuille, Alan and Ni, Bingbing and Zhang, Wenjun and Gao, Wen},
title = {Progressive Stage-Wise Learning for Unsupervised Feature Representation Enhancement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and... | Unsupervised learning methods have recently shown their competitiveness against supervised training. Typically, these methods use a single objective to train the entire network. But one distinct advantage of unsupervised over supervised learning is that the former possesses more variety and freedom in designing the obj... |
Wang_Domain-Specific_Suppression_for_Adaptive_Object_Detection_CVPR_2021_paper | Domain-Specific Suppression for Adaptive Object Detection | [
"Yu Wang",
"Rui Zhang",
"Shuo Zhang",
"Miao Li",
"Yangyang Xia",
"Xishan Zhang",
"Shaoli Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Domain-Specific_Suppression_for_Adaptive_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Domain-Specific_Suppression_for_Adaptive_Object_Detection_CVPR_2021_paper.pdf | null | 2105.03570 | cvf | @InProceedings{Wang_2021_CVPR,
author = {Wang, Yu and Zhang, Rui and Zhang, Shuo and Li, Miao and Xia, Yangyang and Zhang, Xishan and Liu, Shaoli},
title = {Domain-Specific Suppression for Adaptive Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R... | Domain adaptation methods face performance degradation in object detection, as the complexity of tasks require more about the transferability of the model. We propose a new perspective on how CNN models gain the transferability, viewing the weights of a model as a series of motion patterns. The directions of weights, a... |
Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper | Few-Shot Object Detection via Classification Refinement and Distractor Retreatment | [
"Yiting Li",
"Haiyue Zhu",
"Yu Cheng",
"Wenxin Wang",
"Chek Sing Teo",
"Cheng Xiang",
"Prahlad Vadakkepat",
"Tong Heng Lee"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Few-Shot_Object_Detection_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Yiting and Zhu, Haiyue and Cheng, Yu and Wang, Wenxin and Teo, Chek Sing and Xiang, Cheng and Vadakkepat, Prahlad and Lee, Tong Heng},
title = {Few-Shot Object Detection via Classification Refinement and Distractor Retreatment},
booktitle = {Proceedings of t... | We aim to tackle the challenging Few-Shot Object Detection (FSOD) where data-scarce categories are presented during the model learning. The failure modes of FSOD are investigated that the performance degradation is mainly due to the classification incapability (false positives), which motivates us to address it from a ... |
Li_D2IM-Net_Learning_Detail_Disentangled_Implicit_Fields_From_Single_Images_CVPR_2021_paper | D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images | [
"Manyi Li",
"Hao Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_D2IM-Net_Learning_Detail_Disentangled_Implicit_Fields_From_Single_Images_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_D2IM-Net_Learning_Detail_Disentangled_Implicit_Fields_From_Single_Images_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_D2IM-Net_Learning_Detail_CVPR_2021_supplemental.pdf | 2012.06650 | title_judge | @InProceedings{Li_2021_CVPR,
author = {Li, Manyi and Zhang, Hao},
title = {D2IM-Net: Learning Detail Disentangled Implicit Fields From Single Images},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}... | We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field ... |
Kim_Not_Just_Compete_but_Collaborate_Local_Image-to-Image_Translation_via_Cooperative_CVPR_2021_paper | Not Just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction | [
"Daejin Kim",
"Mohammad Azam Khan",
"Jaegul Choo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Not_Just_Compete_but_Collaborate_Local_Image-to-Image_Translation_via_Cooperative_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Not_Just_Compete_but_Collaborate_Local_Image-to-Image_Translation_via_Cooperative_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Kim_2021_CVPR,
author = {Kim, Daejin and Khan, Mohammad Azam and Choo, Jaegul},
title = {Not Just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Facial attribute editing aims to manipulate the image with the desired attribute while preserving the other details. Recently, generative adversarial networks along with the encoder-decoder architecture have been utilized for this task owing to their ability to create realistic images. However, the existing methods for... |
Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper | Behavior-Driven Synthesis of Human Dynamics | [
"Andreas Blattmann",
"Timo Milbich",
"Michael Dorkenwald",
"Bjorn Ommer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Blattmann_Behavior-Driven_Synthesis_of_Human_Dynamics_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Blattmann_Behavior-Driven_Synthesis_of_CVPR_2021_supplemental.zip | 2103.04677 | cvf | @InProceedings{Blattmann_2021_CVPR,
author = {Blattmann, Andreas and Milbich, Timo and Dorkenwald, Michael and Ommer, Bjorn},
title = {Behavior-Driven Synthesis of Human Dynamics},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = ... | Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to ... |
Bu_GAIA_A_Transfer_Learning_System_of_Object_Detection_That_Fits_CVPR_2021_paper | GAIA: A Transfer Learning System of Object Detection That Fits Your Needs | [
"Xingyuan Bu",
"Junran Peng",
"Junjie Yan",
"Tieniu Tan",
"Zhaoxiang Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bu_GAIA_A_Transfer_Learning_System_of_Object_Detection_That_Fits_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bu_GAIA_A_Transfer_Learning_System_of_Object_Detection_That_Fits_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bu_GAIA_A_Transfer_CVPR_2021_supplemental.pdf | 2106.11346 | cvf | @InProceedings{Bu_2021_CVPR,
author = {Bu, Xingyuan and Peng, Junran and Yan, Junjie and Tan, Tieniu and Zhang, Zhaoxiang},
title = {GAIA: A Transfer Learning System of Object Detection That Fits Your Needs},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency constraints and specialized data distributions... |
Kim_IronMask_Modular_Architecture_for_Protecting_Deep_Face_Template_CVPR_2021_paper | IronMask: Modular Architecture for Protecting Deep Face Template | [
"Sunpill Kim",
"Yunseong Jeong",
"Jinsu Kim",
"Jungkon Kim",
"Hyung Tae Lee",
"Jae Hong Seo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_IronMask_Modular_Architecture_for_Protecting_Deep_Face_Template_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_IronMask_Modular_Architecture_for_Protecting_Deep_Face_Template_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_IronMask_Modular_Architecture_CVPR_2021_supplemental.pdf | 2104.02239 | cvf | @InProceedings{Kim_2021_CVPR,
author = {Kim, Sunpill and Jeong, Yunseong and Kim, Jinsu and Kim, Jungkon and Lee, Hyung Tae and Seo, Jae Hong},
title = {IronMask: Modular Architecture for Protecting Deep Face Template},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patter... | Convolutional neural networks have made remarkable progress in the face recognition field. The more the technology of face recognition advances, the greater discriminative features into a face template. However, this increases the threat to user privacy in case the template is exposed. In this paper, we present a modul... |
Yin_Learning_To_Recommend_Frame_for_Interactive_Video_Object_Segmentation_in_CVPR_2021_paper | Learning To Recommend Frame for Interactive Video Object Segmentation in the Wild | [
"Zhaoyuan Yin",
"Jia Zheng",
"Weixin Luo",
"Shenhan Qian",
"Hanling Zhang",
"Shenghua Gao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Learning_To_Recommend_Frame_for_Interactive_Video_Object_Segmentation_in_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Learning_To_Recommend_Frame_for_Interactive_Video_Object_Segmentation_in_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_Learning_To_Recommend_CVPR_2021_supplemental.pdf | 2103.10391 | cvf | @InProceedings{Yin_2021_CVPR,
author = {Yin, Zhaoyuan and Zheng, Jia and Luo, Weixin and Qian, Shenhan and Zhang, Hanling and Gao, Shenghua},
title = {Learning To Recommend Frame for Interactive Video Object Segmentation in the Wild},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vi... | This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluat... |
Hosseini_DSRNA_Differentiable_Search_of_Robust_Neural_Architectures_CVPR_2021_paper | DSRNA: Differentiable Search of Robust Neural Architectures | [
"Ramtin Hosseini",
"Xingyi Yang",
"Pengtao Xie"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hosseini_DSRNA_Differentiable_Search_of_Robust_Neural_Architectures_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hosseini_DSRNA_Differentiable_Search_of_Robust_Neural_Architectures_CVPR_2021_paper.pdf | null | 2012.06122 | cvf | @InProceedings{Hosseini_2021_CVPR,
author = {Hosseini, Ramtin and Yang, Xingyi and Xie, Pengtao},
title = {DSRNA: Differentiable Search of Robust Neural Architectures},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input da... |
Fang_Reconstructing_3D_Human_Pose_by_Watching_Humans_in_the_Mirror_CVPR_2021_paper | Reconstructing 3D Human Pose by Watching Humans in the Mirror | [
"Qi Fang",
"Qing Shuai",
"Junting Dong",
"Hujun Bao",
"Xiaowei Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fang_Reconstructing_3D_Human_Pose_by_Watching_Humans_in_the_Mirror_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fang_Reconstructing_3D_Human_Pose_by_Watching_Humans_in_the_Mirror_CVPR_2021_paper.pdf | null | 2104.00340 | cvf | @InProceedings{Fang_2021_CVPR,
author = {Fang, Qi and Shuai, Qing and Dong, Junting and Bao, Hujun and Zhou, Xiaowei},
title = {Reconstructing 3D Human Pose by Watching Humans in the Mirror},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror. Compared to general scenarios of 3D pose estimation from a single view, the mirror reflection provides an additional view for resolving the depth ambiguity... |
Zhao_Spk2ImgNet_Learning_To_Reconstruct_Dynamic_Scene_From_Continuous_Spike_Stream_CVPR_2021_paper | Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream | [
"Jing Zhao",
"Ruiqin Xiong",
"Hangfan Liu",
"Jian Zhang",
"Tiejun Huang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Spk2ImgNet_Learning_To_Reconstruct_Dynamic_Scene_From_Continuous_Spike_Stream_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Spk2ImgNet_Learning_To_Reconstruct_Dynamic_Scene_From_Continuous_Spike_Stream_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Spk2ImgNet_Learning_To_CVPR_2021_supplemental.zip | null | null | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Jing and Xiong, Ruiqin and Liu, Hangfan and Zhang, Jian and Huang, Tiejun},
title = {Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog... | The recently invented retina-inspired spike camera has shown great potential for capturing dynamic scenes. Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dyn... |
Chen_MonoRUn_Monocular_3D_Object_Detection_by_Reconstruction_and_Uncertainty_Propagation_CVPR_2021_paper | MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation | [
"Hansheng Chen",
"Yuyao Huang",
"Wei Tian",
"Zhong Gao",
"Lu Xiong"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_MonoRUn_Monocular_3D_Object_Detection_by_Reconstruction_and_Uncertainty_Propagation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_MonoRUn_Monocular_3D_Object_Detection_by_Reconstruction_and_Uncertainty_Propagation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_MonoRUn_Monocular_3D_CVPR_2021_supplemental.pdf | 2103.12605 | cvf | @InProceedings{Chen_2021_CVPR,
author = {Chen, Hansheng and Huang, Yuyao and Tian, Wei and Gao, Zhong and Xiong, Lu},
title = {MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Reco... | Object localization in 3D space is a challenging aspect in monocular 3D object detection. Recent advances in 6DoF pose estimation have shown that predicting dense 2D-3D correspondence maps between image and object 3D model and then estimating object pose via Perspective-n-Point (PnP) algorithm can achieve remarkable lo... |
Yi_Complete__Label_A_Domain_Adaptation_Approach_to_Semantic_Segmentation_CVPR_2021_paper | Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds | [
"Li Yi",
"Boqing Gong",
"Thomas Funkhouser"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yi_Complete__Label_A_Domain_Adaptation_Approach_to_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yi_Complete__Label_A_Domain_Adaptation_Approach_to_Semantic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yi_Complete__Label_CVPR_2021_supplemental.pdf | 2007.08488 | cvf | @InProceedings{Yi_2021_CVPR,
author = {Yi, Li and Gong, Boqing and Funkhouser, Thomas},
title = {Complete \& Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | We study an unsupervised domain adaptation problem for the semantic labeling of 3D point clouds, with a particular focus on domain discrepancies induced by different LiDAR sensors. Based on the observation that sparse 3D point clouds are sampled from 3D surfaces, we take a Complete and Label approach to recover the und... |
Bai_GMOT-40_A_Benchmark_for_Generic_Multiple_Object_Tracking_CVPR_2021_paper | GMOT-40: A Benchmark for Generic Multiple Object Tracking | [
"Hexin Bai",
"Wensheng Cheng",
"Peng Chu",
"Juehuan Liu",
"Kai Zhang",
"Haibin Ling"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Bai_GMOT-40_A_Benchmark_for_Generic_Multiple_Object_Tracking_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Bai_GMOT-40_A_Benchmark_for_Generic_Multiple_Object_Tracking_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Bai_GMOT-40_A_Benchmark_CVPR_2021_supplemental.pdf | 2011.11858 | title_snapshot | @InProceedings{Bai_2021_CVPR,
author = {Bai, Hexin and Cheng, Wensheng and Chu, Peng and Liu, Juehuan and Zhang, Kai and Ling, Haibin},
title = {GMOT-40: A Benchmark for Generic Multiple Object Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (... | Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years. However, existing studies dominantly request prior knowledge of the tracking target (eg, pedestrians), and hence may not generalize well to unseen categories. In contrast, Generic Multiple Object Tracking (GMOT), which requires little pri... |
Ojha_Few-Shot_Image_Generation_via_Cross-Domain_Correspondence_CVPR_2021_paper | Few-Shot Image Generation via Cross-Domain Correspondence | [
"Utkarsh Ojha",
"Yijun Li",
"Jingwan Lu",
"Alexei A. Efros",
"Yong Jae Lee",
"Eli Shechtman",
"Richard Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ojha_Few-Shot_Image_Generation_via_Cross-Domain_Correspondence_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ojha_Few-Shot_Image_Generation_via_Cross-Domain_Correspondence_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ojha_Few-Shot_Image_Generation_CVPR_2021_supplemental.pdf | 2104.06820 | cvf | @InProceedings{Ojha_2021_CVPR,
author = {Ojha, Utkarsh and Li, Yijun and Lu, Jingwan and Efros, Alexei A. and Lee, Yong Jae and Shechtman, Eli and Zhang, Richard},
title = {Few-Shot Image Generation via Cross-Domain Correspondence},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. We propose to preserve the relative similarities and ... |
Kerola_Hierarchical_Lovasz_Embeddings_for_Proposal-Free_Panoptic_Segmentation_CVPR_2021_paper | Hierarchical Lovasz Embeddings for Proposal-Free Panoptic Segmentation | [
"Tommi Kerola",
"Jie Li",
"Atsushi Kanehira",
"Yasunori Kudo",
"Alexis Vallet",
"Adrien Gaidon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kerola_Hierarchical_Lovasz_Embeddings_for_Proposal-Free_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kerola_Hierarchical_Lovasz_Embeddings_for_Proposal-Free_Panoptic_Segmentation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kerola_Hierarchical_Lovasz_Embeddings_CVPR_2021_supplemental.pdf | 2106.04555 | cvf | @InProceedings{Kerola_2021_CVPR,
author = {Kerola, Tommi and Li, Jie and Kanehira, Atsushi and Kudo, Yasunori and Vallet, Alexis and Gaidon, Adrien},
title = {Hierarchical Lovasz Embeddings for Proposal-Free Panoptic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visio... | Panoptic segmentation brings together two separate tasks: instance and semantic segmentation. Although they are related, unifying them faces an apparent paradox: how to learn simultaneously instance-specific and category-specific (i.e. instance-agnostic) representations jointly. Hence, state-of-the-art panoptic segment... |
Peng_Neural_Body_Implicit_Neural_Representations_With_Structured_Latent_Codes_for_CVPR_2021_paper | Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans | [
"Sida Peng",
"Yuanqing Zhang",
"Yinghao Xu",
"Qianqian Wang",
"Qing Shuai",
"Hujun Bao",
"Xiaowei Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Peng_Neural_Body_Implicit_Neural_Representations_With_Structured_Latent_Codes_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Peng_Neural_Body_Implicit_Neural_Representations_With_Structured_Latent_Codes_for_CVPR_2021_paper.pdf | null | 2012.15838 | cvf | @InProceedings{Peng_2021_CVPR,
author = {Peng, Sida and Zhang, Yuanqing and Xu, Yinghao and Wang, Qianqian and Shuai, Qing and Bao, Hujun and Zhou, Xiaowei},
title = {Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans},
booktitle = {Pr... | This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will b... |
Liu_Cross-Modal_Collaborative_Representation_Learning_and_a_Large-Scale_RGBT_Benchmark_for_CVPR_2021_paper | Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting | [
"Lingbo Liu",
"Jiaqi Chen",
"Hefeng Wu",
"Guanbin Li",
"Chenglong Li",
"Liang Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Cross-Modal_Collaborative_Representation_Learning_and_a_Large-Scale_RGBT_Benchmark_for_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Cross-Modal_Collaborative_Representation_Learning_and_a_Large-Scale_RGBT_Benchmark_for_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Cross-Modal_Collaborative_Representation_CVPR_2021_supplemental.pdf | 2012.04529 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Lingbo and Chen, Jiaqi and Wu, Hefeng and Li, Guanbin and Li, Chenglong and Lin, Liang},
title = {Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting},
booktitle = {Proceedings of the IEEE/CVF Conference on... | Crowd counting is a fundamental yet challenging task, which desires rich information to generate pixel-wise crowd density maps. However, most previous methods only used the limited information of RGB images and cannot well discover potential pedestrians in unconstrained scenarios. In this work, we find that incorporati... |
Zhao_Weakly_Supervised_Video_Salient_Object_Detection_CVPR_2021_paper | Weakly Supervised Video Salient Object Detection | [
"Wangbo Zhao",
"Jing Zhang",
"Long Li",
"Nick Barnes",
"Nian Liu",
"Junwei Han"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Weakly_Supervised_Video_Salient_Object_Detection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Weakly_Supervised_Video_Salient_Object_Detection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Weakly_Supervised_Video_CVPR_2021_supplemental.pdf | 2104.02391 | cvf | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Wangbo and Zhang, Jing and Li, Long and Barnes, Nick and Liu, Nian and Han, Junwei},
title = {Weakly Supervised Video Salient Object Detection},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
m... | Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are timeconsuming and expensive to obtain. To relieve the burden of data annotation, we present the first weakly supervised video salient object detection model ... |
Di_Biase_Pixel-Wise_Anomaly_Detection_in_Complex_Driving_Scenes_CVPR_2021_paper | Pixel-Wise Anomaly Detection in Complex Driving Scenes | [
"Giancarlo Di Biase",
"Hermann Blum",
"Roland Siegwart",
"Cesar Cadena"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Di_Biase_Pixel-Wise_Anomaly_Detection_in_Complex_Driving_Scenes_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Di_Biase_Pixel-Wise_Anomaly_Detection_in_Complex_Driving_Scenes_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Di_Biase_Pixel-Wise_Anomaly_Detection_CVPR_2021_supplemental.pdf | 2103.05445 | cvf | @InProceedings{Di_Biase_2021_CVPR,
author = {Di Biase, Giancarlo and Blum, Hermann and Siegwart, Roland and Cadena, Cesar},
title = {Pixel-Wise Anomaly Detection in Complex Driving Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mon... | The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving. Recent approaches have focused on either leveraging segmentation uncertainty to identify anomalous areas or re-synthesizin... |
Woo_Learning_To_Associate_Every_Segment_for_Video_Panoptic_Segmentation_CVPR_2021_paper | Learning To Associate Every Segment for Video Panoptic Segmentation | [
"Sanghyun Woo",
"Dahun Kim",
"Joon-Young Lee",
"In So Kweon"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Woo_Learning_To_Associate_Every_Segment_for_Video_Panoptic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Woo_Learning_To_Associate_Every_Segment_for_Video_Panoptic_Segmentation_CVPR_2021_paper.pdf | null | 2106.09453 | cvf | @InProceedings{Woo_2021_CVPR,
author = {Woo, Sanghyun and Kim, Dahun and Lee, Joon-Young and Kweon, In So},
title = {Learning To Associate Every Segment for Video Panoptic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | Temporal correspondence -- linking pixels or objects across frames -- is a fundamental supervisory signal for the video models. For the panoptic understanding of dynamic scenes, we further extend this concept to every segment. Specifically, we aim to learn coarse segment-level matching and fine pixel-level matching tog... |
Arroyo_Variational_Transformer_Networks_for_Layout_Generation_CVPR_2021_paper | Variational Transformer Networks for Layout Generation | [
"Diego Martin Arroyo",
"Janis Postels",
"Federico Tombari"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Arroyo_Variational_Transformer_Networks_for_Layout_Generation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Arroyo_Variational_Transformer_Networks_for_Layout_Generation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Arroyo_Variational_Transformer_Networks_CVPR_2021_supplemental.zip | 2104.02416 | cvf | @InProceedings{Arroyo_2021_CVPR,
author = {Arroyo, Diego Martin and Postels, Janis and Tombari, Federico},
title = {Variational Transformer Networks for Layout Generation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Generative models able to synthesize layouts of different kinds (e.g. documents, user interfaces or furniture arrangements) are a useful tool to aid design processes and as a first step in the generation of synthetic data, among other tasks. We exploit the properties of self-attention layers to capture high level relat... |
Gong_Mitigating_Face_Recognition_Bias_via_Group_Adaptive_Classifier_CVPR_2021_paper | Mitigating Face Recognition Bias via Group Adaptive Classifier | [
"Sixue Gong",
"Xiaoming Liu",
"Anil K. Jain"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Gong_Mitigating_Face_Recognition_Bias_via_Group_Adaptive_Classifier_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Gong_Mitigating_Face_Recognition_Bias_via_Group_Adaptive_Classifier_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Gong_Mitigating_Face_Recognition_CVPR_2021_supplemental.pdf | 2006.07576 | cvf | @InProceedings{Gong_2021_CVPR,
author = {Gong, Sixue and Liu, Xiaoming and Jain, Anil K.},
title = {Mitigating Face Recognition Bias via Group Adaptive Classifier},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year... | Face recognition is known to exhibit bias -- subjects in a certain demographic group can be better recognized than other groups. This work aims to learn a fair face representation, where faces of every group could be more equally represented. Our proposed group adaptive classifier mitigates bias by using adaptive convo... |
Ge_A_Peek_Into_the_Reasoning_of_Neural_Networks_Interpreting_With_CVPR_2021_paper | A Peek Into the Reasoning of Neural Networks: Interpreting With Structural Visual Concepts | [
"Yunhao Ge",
"Yao Xiao",
"Zhi Xu",
"Meng Zheng",
"Srikrishna Karanam",
"Terrence Chen",
"Laurent Itti",
"Ziyan Wu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ge_A_Peek_Into_the_Reasoning_of_Neural_Networks_Interpreting_With_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ge_A_Peek_Into_the_Reasoning_of_Neural_Networks_Interpreting_With_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ge_A_Peek_Into_CVPR_2021_supplemental.pdf | 2105.00290 | cvf | @InProceedings{Ge_2021_CVPR,
author = {Ge, Yunhao and Xiao, Yao and Xu, Zhi and Zheng, Meng and Karanam, Srikrishna and Chen, Terrence and Itti, Laurent and Wu, Ziyan},
title = {A Peek Into the Reasoning of Neural Networks: Interpreting With Structural Visual Concepts},
booktitle = {Proceedings of th... | Despite substantial progress in applying neural networks (NN) to a wide variety of areas, they still largely suffer from a lack of transparency and interpretability. While recent developments in explainable artificial intelligence attempt to bridge this gap (e.g., by visualizing the correlation between input pixels and... |
Li_Three_Birds_with_One_Stone_Multi-Task_Temporal_Action_Detection_via_CVPR_2021_paper | Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations | [
"Zhihui Li",
"Lina Yao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Three_Birds_with_One_Stone_Multi-Task_Temporal_Action_Detection_via_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Three_Birds_with_One_Stone_Multi-Task_Temporal_Action_Detection_via_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Li_2021_CVPR,
author = {Li, Zhihui and Yao, Lina},
title = {Three Birds with One Stone: Multi-Task Temporal Action Detection via Recycling Temporal Annotations},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun... | Temporal action detection on unconstrained videos has seen significant research progress in recent years. Deep learning has achieved enormous success in this direction. However, collecting large-scale temporal detection datasets to ensuring promising performance in the real-world is a laborious, impractical and time co... |
Xiang_A_Dual_Iterative_Refinement_Method_for_Non-Rigid_Shape_Matching_CVPR_2021_paper | A Dual Iterative Refinement Method for Non-Rigid Shape Matching | [
"Rui Xiang",
"Rongjie Lai",
"Hongkai Zhao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Xiang_A_Dual_Iterative_Refinement_Method_for_Non-Rigid_Shape_Matching_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Xiang_A_Dual_Iterative_Refinement_Method_for_Non-Rigid_Shape_Matching_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Xiang_A_Dual_Iterative_CVPR_2021_supplemental.pdf | 2007.13049 | cvf | @InProceedings{Xiang_2021_CVPR,
author = {Xiang, Rui and Lai, Rongjie and Zhao, Hongkai},
title = {A Dual Iterative Refinement Method for Non-Rigid Shape Matching},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year... | In this work, a robust and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes. The key idea is to use dual information, such as spatial and spectral, or local and global features, in a complementary and effective way, and extract more accurate infor... |
Mei_Image_Super-Resolution_With_Non-Local_Sparse_Attention_CVPR_2021_paper | Image Super-Resolution With Non-Local Sparse Attention | [
"Yiqun Mei",
"Yuchen Fan",
"Yuqian Zhou"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Mei_Image_Super-Resolution_With_Non-Local_Sparse_Attention_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Mei_Image_Super-Resolution_With_Non-Local_Sparse_Attention_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Mei_Image_Super-Resolution_With_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Mei_2021_CVPR,
author = {Mei, Yiqun and Fan, Yuchen and Zhou, Yuqian},
title = {Image Super-Resolution With Non-Local Sparse Attention},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | Both non-local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern. NLSA is designed to retain long-range modeling capability from NL op... |
Lee_3D_Video_Stabilization_With_Depth_Estimation_by_CNN-Based_Optimization_CVPR_2021_paper | 3D Video Stabilization With Depth Estimation by CNN-Based Optimization | [
"Yao-Chih Lee",
"Kuan-Wei Tseng",
"Yu-Ta Chen",
"Chien-Cheng Chen",
"Chu-Song Chen",
"Yi-Ping Hung"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_3D_Video_Stabilization_With_Depth_Estimation_by_CNN-Based_Optimization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_3D_Video_Stabilization_With_Depth_Estimation_by_CNN-Based_Optimization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_3D_Video_Stabilization_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Lee_2021_CVPR,
author = {Lee, Yao-Chih and Tseng, Kuan-Wei and Chen, Yu-Ta and Chen, Chien-Cheng and Chen, Chu-Song and Hung, Yi-Ping},
title = {3D Video Stabilization With Depth Estimation by CNN-Based Optimization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | Video stabilization is an essential component of visual quality enhancement. Early methods rely on feature tracking to recover either 2D or 3D frame motion, which suffer from the robustness of local feature extraction and tracking in shaky videos. Recently, learning-based methods seek to find frame transformations with... |
Chen_Predicting_Human_Scanpaths_in_Visual_Question_Answering_CVPR_2021_paper | Predicting Human Scanpaths in Visual Question Answering | [
"Xianyu Chen",
"Ming Jiang",
"Qi Zhao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_Predicting_Human_Scanpaths_in_Visual_Question_Answering_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Predicting_Human_Scanpaths_in_Visual_Question_Answering_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_Predicting_Human_Scanpaths_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Chen_2021_CVPR,
author = {Chen, Xianyu and Jiang, Ming and Zhao, Qi},
title = {Predicting Human Scanpaths in Visual Question Answering},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | Attention has been an important mechanism for both humans and computer vision systems. While state-of-the-art models to predict attention focus on estimating a static probabilistic saliency map with free-viewing behavior, real-life scenarios are filled with tasks of varying types and complexities, and visual exploratio... |
Qiao_DetectoRS_Detecting_Objects_With_Recursive_Feature_Pyramid_and_Switchable_Atrous_CVPR_2021_paper | DetectoRS: Detecting Objects With Recursive Feature Pyramid and Switchable Atrous Convolution | [
"Siyuan Qiao",
"Liang-Chieh Chen",
"Alan Yuille"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_DetectoRS_Detecting_Objects_With_Recursive_Feature_Pyramid_and_Switchable_Atrous_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiao_DetectoRS_Detecting_Objects_With_Recursive_Feature_Pyramid_and_Switchable_Atrous_CVPR_2021_paper.pdf | null | 2006.02334 | cvf | @InProceedings{Qiao_2021_CVPR,
author = {Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan},
title = {DetectoRS: Detecting Objects With Recursive Feature Pyramid and Switchable Atrous Convolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)... | Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Featu... |
Saito_SCANimate_Weakly_Supervised_Learning_of_Skinned_Clothed_Avatar_Networks_CVPR_2021_paper | SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks | [
"Shunsuke Saito",
"Jinlong Yang",
"Qianli Ma",
"Michael J. Black"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Saito_SCANimate_Weakly_Supervised_Learning_of_Skinned_Clothed_Avatar_Networks_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Saito_SCANimate_Weakly_Supervised_Learning_of_Skinned_Clothed_Avatar_Networks_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Saito_SCANimate_Weakly_Supervised_CVPR_2021_supplemental.pdf | 2104.03313 | cvf | @InProceedings{Saito_2021_CVPR,
author = {Saito, Shunsuke and Yang, Jinlong and Ma, Qianli and Black, Michael J.},
title = {SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}... | We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar. These avatars are driven by pose parameters and have realistic clothing that moves and deforms naturally. SCANimate does not rely on a customized mesh template or surface mesh reg... |
Kim_Improving_Accuracy_of_Binary_Neural_Networks_Using_Unbalanced_Activation_Distribution_CVPR_2021_paper | Improving Accuracy of Binary Neural Networks Using Unbalanced Activation Distribution | [
"Hyungjun Kim",
"Jihoon Park",
"Changhun Lee",
"Jae-Joon Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Kim_Improving_Accuracy_of_Binary_Neural_Networks_Using_Unbalanced_Activation_Distribution_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Kim_Improving_Accuracy_of_Binary_Neural_Networks_Using_Unbalanced_Activation_Distribution_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Kim_Improving_Accuracy_of_CVPR_2021_supplemental.pdf | 2012.00938 | cvf | @InProceedings{Kim_2021_CVPR,
author = {Kim, Hyungjun and Park, Jihoon and Lee, Changhun and Kim, Jae-Joon},
title = {Improving Accuracy of Binary Neural Networks Using Unbalanced Activation Distribution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ... | Binarization of neural network models is considered as one of the promising methods to deploy deep neural network models on resource-constrained environments such as mobile devices. However, Binary Neural Networks (BNNs) tend to suffer from severe accuracy degradation compared to the full-precision counterpart model. S... |
Zhu_Cylindrical_and_Asymmetrical_3D_Convolution_Networks_for_LiDAR_Segmentation_CVPR_2021_paper | Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation | [
"Xinge Zhu",
"Hui Zhou",
"Tai Wang",
"Fangzhou Hong",
"Yuexin Ma",
"Wei Li",
"Hongsheng Li",
"Dahua Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhu_Cylindrical_and_Asymmetrical_3D_Convolution_Networks_for_LiDAR_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Cylindrical_and_Asymmetrical_3D_Convolution_Networks_for_LiDAR_Segmentation_CVPR_2021_paper.pdf | null | 2011.10033 | cvf | @InProceedings{Zhu_2021_CVPR,
author = {Zhu, Xinge and Zhou, Hui and Wang, Tai and Hong, Fangzhou and Ma, Yuexin and Li, Wei and Li, Hongsheng and Lin, Dahua},
title = {Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on... | State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the point clouds to 2D space and then process them via 2D convolution. Although this corporation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural reme... |
Corona_SMPLicit_Topology-Aware_Generative_Model_for_Clothed_People_CVPR_2021_paper | SMPLicit: Topology-Aware Generative Model for Clothed People | [
"Enric Corona",
"Albert Pumarola",
"Guillem Alenya",
"Gerard Pons-Moll",
"Francesc Moreno-Noguer"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Corona_SMPLicit_Topology-Aware_Generative_Model_for_Clothed_People_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Corona_SMPLicit_Topology-Aware_Generative_Model_for_Clothed_People_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Corona_SMPLicit_Topology-Aware_Generative_CVPR_2021_supplemental.pdf | 2103.06871 | cvf | @InProceedings{Corona_2021_CVPR,
author = {Corona, Enric and Pumarola, Albert and Alenya, Guillem and Pons-Moll, Gerard and Moreno-Noguer, Francesc},
title = {SMPLicit: Topology-Aware Generative Model for Clothed People},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patt... | In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (e.g. from ... |
Zhao_Learning_View-Disentangled_Human_Pose_Representation_by_Contrastive_Cross-View_Mutual_Information_CVPR_2021_paper | Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization | [
"Long Zhao",
"Yuxiao Wang",
"Jiaping Zhao",
"Liangzhe Yuan",
"Jennifer J. Sun",
"Florian Schroff",
"Hartwig Adam",
"Xi Peng",
"Dimitris Metaxas",
"Ting Liu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Zhao_Learning_View-Disentangled_Human_Pose_Representation_by_Contrastive_Cross-View_Mutual_Information_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Zhao_Learning_View-Disentangled_Human_Pose_Representation_by_Contrastive_Cross-View_Mutual_Information_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Zhao_Learning_View-Disentangled_Human_CVPR_2021_supplemental.pdf | 2012.01405 | cvf | @InProceedings{Zhao_2021_CVPR,
author = {Zhao, Long and Wang, Yuxiao and Zhao, Jiaping and Yuan, Liangzhe and Sun, Jennifer J. and Schroff, Florian and Adam, Hartwig and Peng, Xi and Metaxas, Dimitris and Liu, Ting},
title = {Learning View-Disentangled Human Pose Representation by Contrastive Cross-View ... | We introduce a novel representation learning method to disentangle pose-dependent as well as view-dependent factors from 2D human poses. The method trains a network using cross-view mutual information maximization (CV-MIM) which maximizes mutual information of the same pose performed from different viewpoints in a cont... |
Yao_Non-Salient_Region_Object_Mining_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper | Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation | [
"Yazhou Yao",
"Tao Chen",
"Guo-Sen Xie",
"Chuanyi Zhang",
"Fumin Shen",
"Qi Wu",
"Zhenmin Tang",
"Jian Zhang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Non-Salient_Region_Object_Mining_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_Non-Salient_Region_Object_Mining_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2021_paper.pdf | null | 2103.14581 | cvf | @InProceedings{Yao_2021_CVPR,
author = {Yao, Yazhou and Chen, Tao and Xie, Guo-Sen and Zhang, Chuanyi and Shen, Fumin and Wu, Qi and Tang, Zhenmin and Zhang, Jian},
title = {Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Confere... | Semantic segmentation aims to classify every pixel of an input image. Considering the difficulty of acquiring dense labels, researchers have recently been resorting to weak labels to alleviate the annotation burden of segmentation. However, existing works mainly concentrate on expanding the seed of pseudo labels within... |
Shen_DCT-Mask_Discrete_Cosine_Transform_Mask_Representation_for_Instance_Segmentation_CVPR_2021_paper | DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation | [
"Xing Shen",
"Jirui Yang",
"Chunbo Wei",
"Bing Deng",
"Jianqiang Huang",
"Xian-Sheng Hua",
"Xiaoliang Cheng",
"Kewei Liang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Shen_DCT-Mask_Discrete_Cosine_Transform_Mask_Representation_for_Instance_Segmentation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Shen_DCT-Mask_Discrete_Cosine_Transform_Mask_Representation_for_Instance_Segmentation_CVPR_2021_paper.pdf | null | 2011.09876 | title_snapshot | @InProceedings{Shen_2021_CVPR,
author = {Shen, Xing and Yang, Jirui and Wei, Chunbo and Deng, Bing and Huang, Jianqiang and Hua, Xian-Sheng and Cheng, Xiaoliang and Liang, Kewei},
title = {DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation},
booktitle = {Proceedings of ... | Binary grid mask representation is broadly used in instance segmentation. A representative instantiation is Mask R-CNN which predicts masks on a 28*28 binary grid. Generally, a low-resolution grid is not sufficient to capture the details, while a high-resolution grid dramatically increases the training complexity. In t... |
Lu_Bridging_the_Visual_Gap_Wide-Range_Image_Blending_CVPR_2021_paper | Bridging the Visual Gap: Wide-Range Image Blending | [
"Chia-Ni Lu",
"Ya-Chu Chang",
"Wei-Chen Chiu"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lu_Bridging_the_Visual_Gap_Wide-Range_Image_Blending_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lu_Bridging_the_Visual_Gap_Wide-Range_Image_Blending_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lu_Bridging_the_Visual_CVPR_2021_supplemental.pdf | 2103.15149 | cvf | @InProceedings{Lu_2021_CVPR,
author = {Lu, Chia-Ni and Chang, Ya-Chu and Chiu, Wei-Chen},
title = {Bridging the Visual Gap: Wide-Range Image Blending},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021... | In this paper we propose a new problem scenario in image processing, wide-range image blending, which aims to smoothly merge two different input photos into a panorama by generating novel image content for the intermediate region between them. Although such problem is closely related to the topics of image inpainting, ... |
Su_A_Realistic_Evaluation_of_Semi-Supervised_Learning_for_Fine-Grained_Classification_CVPR_2021_paper | A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification | [
"Jong-Chyi Su",
"Zezhou Cheng",
"Subhransu Maji"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Su_A_Realistic_Evaluation_of_Semi-Supervised_Learning_for_Fine-Grained_Classification_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Su_A_Realistic_Evaluation_of_Semi-Supervised_Learning_for_Fine-Grained_Classification_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Su_A_Realistic_Evaluation_CVPR_2021_supplemental.pdf | 2104.00679 | cvf | @InProceedings{Su_2021_CVPR,
author = {Su, Jong-Chyi and Cheng, Zezhou and Maji, Subhransu},
title = {A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month ... | We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes. Our benchmark consists of two fine-grained classification datasets obtained by sampling classes from the Aves and Fungi taxonomy. We find that... |
Lv_Residential_Floor_Plan_Recognition_and_Reconstruction_CVPR_2021_paper | Residential Floor Plan Recognition and Reconstruction | [
"Xiaolei Lv",
"Shengchu Zhao",
"Xinyang Yu",
"Binqiang Zhao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lv_Residential_Floor_Plan_Recognition_and_Reconstruction_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lv_Residential_Floor_Plan_Recognition_and_Reconstruction_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lv_Residential_Floor_Plan_CVPR_2021_supplemental.pdf | null | null | @InProceedings{Lv_2021_CVPR,
author = {Lv, Xiaolei and Zhao, Shengchu and Yu, Xinyang and Zhao, Binqiang},
title = {Residential Floor Plan Recognition and Reconstruction},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
... | Recognition and reconstruction of residential floor plan drawings are important and challenging in design, decoration, and architectural remodeling fields. An automatic framework is provided that accurately recognizes the structure, type, and size of the room, and outputs vectorized 3D reconstruction results. Deep segm... |
Li_Dynamic_Domain_Adaptation_for_Efficient_Inference_CVPR_2021_paper | Dynamic Domain Adaptation for Efficient Inference | [
"Shuang Li",
"JinMing Zhang",
"Wenxuan Ma",
"Chi Harold Liu",
"Wei Li"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Domain_Adaptation_for_Efficient_Inference_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Dynamic_Domain_Adaptation_for_Efficient_Inference_CVPR_2021_paper.pdf | null | 2103.16403 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Shuang and Zhang, JinMing and Ma, Wenxuan and Liu, Chi Harold and Li, Wei},
title = {Dynamic Domain Adaptation for Efficient Inference},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {... | Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA approaches leverage complicated and powerful deep neural networks to improve the adaptation capacity and have shown remarkable success. Howeve... |
Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper | Regularization Strategy for Point Cloud via Rigidly Mixed Sample | [
"Dogyoon Lee",
"Jaeha Lee",
"Junhyeop Lee",
"Hyeongmin Lee",
"Minhyeok Lee",
"Sungmin Woo",
"Sangyoun Lee"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Lee_Regularization_Strategy_for_CVPR_2021_supplemental.pdf | 2102.01929 | cvf | @InProceedings{Lee_2021_CVPR,
author = {Lee, Dogyoon and Lee, Jaeha and Lee, Junhyeop and Lee, Hyeongmin and Lee, Minhyeok and Woo, Sungmin and Lee, Sangyoun},
title = {Regularization Strategy for Point Cloud via Rigidly Mixed Sample},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer V... | Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rarely considered for point cloud processing despite many studies proposing various augmentation methods for image data. Actually, regularizati... |
Hong_StereoPIFu_Depth_Aware_Clothed_Human_Digitization_via_Stereo_Vision_CVPR_2021_paper | StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision | [
"Yang Hong",
"Juyong Zhang",
"Boyi Jiang",
"Yudong Guo",
"Ligang Liu",
"Hujun Bao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Hong_StereoPIFu_Depth_Aware_Clothed_Human_Digitization_via_Stereo_Vision_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Hong_StereoPIFu_Depth_Aware_Clothed_Human_Digitization_via_Stereo_Vision_CVPR_2021_paper.pdf | null | 2104.05289 | cvf | @InProceedings{Hong_2021_CVPR,
author = {Hong, Yang and Zhang, Juyong and Jiang, Boyi and Guo, Yudong and Liu, Ligang and Bao, Hujun},
title = {StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec... | In this paper, we propose StereoPIFu, which integrates the geometric constraints of stereo vision with implicit function representation of PIFu, to recover the 3D shape of the clothed human from a pair of low-cost rectified images. First, we introduce the effective voxel-aligned features from a stereo vision-based netw... |
Ahmed_Unsupervised_Multi-Source_Domain_Adaptation_Without_Access_to_Source_Data_CVPR_2021_paper | Unsupervised Multi-Source Domain Adaptation Without Access to Source Data | [
"Sk Miraj Ahmed",
"Dripta S. Raychaudhuri",
"Sujoy Paul",
"Samet Oymak",
"Amit K. Roy-Chowdhury"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Ahmed_Unsupervised_Multi-Source_Domain_Adaptation_Without_Access_to_Source_Data_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Ahmed_Unsupervised_Multi-Source_Domain_Adaptation_Without_Access_to_Source_Data_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Ahmed_Unsupervised_Multi-Source_Domain_CVPR_2021_supplemental.pdf | 2104.01845 | cvf | @InProceedings{Ahmed_2021_CVPR,
author = {Ahmed, Sk Miraj and Raychaudhuri, Dripta S. and Paul, Sujoy and Oymak, Samet and Roy-Chowdhury, Amit K.},
title = {Unsupervised Multi-Source Domain Adaptation Without Access to Source Data},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Visi... | Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled dataset by transferring knowledge from a labeled source data, which has been trained on similar tasks. However, most of these conventional UDA approaches have a strong assumption of having access to the source data during training, wh... |
Wray_On_Semantic_Similarity_in_Video_Retrieval_CVPR_2021_paper | On Semantic Similarity in Video Retrieval | [
"Michael Wray",
"Hazel Doughty",
"Dima Damen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wray_On_Semantic_Similarity_in_Video_Retrieval_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wray_On_Semantic_Similarity_in_Video_Retrieval_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Wray_On_Semantic_Similarity_CVPR_2021_supplemental.pdf | 2103.10095 | cvf | @InProceedings{Wray_2021_CVPR,
author = {Wray, Michael and Doughty, Hazel and Damen, Dima},
title = {On Semantic Similarity in Video Retrieval},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
... | Current video retrieval efforts all found their evaluation on an instance-based assumption, that only a single caption is relevant to a query video and vice versa. We demonstrate that this assumption results in performance comparisons often not indicative of models' retrieval capabilities. We propose a move to semantic... |
Jeong_Few-Shot_Open-Set_Recognition_by_Transformation_Consistency_CVPR_2021_paper | Few-Shot Open-Set Recognition by Transformation Consistency | [
"Minki Jeong",
"Seokeon Choi",
"Changick Kim"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Jeong_Few-Shot_Open-Set_Recognition_by_Transformation_Consistency_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Jeong_Few-Shot_Open-Set_Recognition_by_Transformation_Consistency_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Jeong_Few-Shot_Open-Set_Recognition_CVPR_2021_supplemental.pdf | 2103.01537 | cvf | @InProceedings{Jeong_2021_CVPR,
author = {Jeong, Minki and Choi, Seokeon and Kim, Changick},
title = {Few-Shot Open-Set Recognition by Transformation Consistency},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | In this paper, we attack a few-shot open-set recognition (FSOSR) problem, which is a combination of few-shot learning (FSL) and open-set recognition (OSR). It aims to quickly adapt a model to a given small set of labeled samples while rejecting unseen class samples. Since OSR requires rich data and FSL considers closed... |
Qiao_Uncertainty-Guided_Model_Generalization_to_Unseen_Domains_CVPR_2021_paper | Uncertainty-Guided Model Generalization to Unseen Domains | [
"Fengchun Qiao",
"Xi Peng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_Uncertainty-Guided_Model_Generalization_to_Unseen_Domains_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Qiao_Uncertainty-Guided_Model_Generalization_to_Unseen_Domains_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Qiao_Uncertainty-Guided_Model_Generalization_CVPR_2021_supplemental.pdf | 2103.07531 | cvf | @InProceedings{Qiao_2021_CVPR,
author = {Qiao, Fengchun and Peng, Xi},
title = {Uncertainty-Guided Model Generalization to Unseen Domains},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages... | We study a worst-case scenario in generalization: Out-of-domain generalization from a single source. The goal is to learn a robust model from a single source and expect it to generalize over many unknown distributions. This challenging problem has been seldom investigated while existing solutions suffer from various li... |
Cao_Debiased_Subjective_Assessment_of_Real-World_Image_Enhancement_CVPR_2021_paper | Debiased Subjective Assessment of Real-World Image Enhancement | [
"Peibei Cao",
"Zhangyang Wang",
"Kede Ma"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Cao_Debiased_Subjective_Assessment_of_Real-World_Image_Enhancement_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Cao_Debiased_Subjective_Assessment_of_Real-World_Image_Enhancement_CVPR_2021_paper.pdf | null | 2106.10080 | cvf | @InProceedings{Cao_2021_CVPR,
author = {Cao, Peibei and Wang, Zhangyang and Ma, Kede},
title = {Debiased Subjective Assessment of Real-World Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | In real-world image enhancement, it is often challenging (if not impossible) to acquire ground-truth data, preventing the adoption of distance metrics for objective quality assessment. As a result, one often resorts to subjective quality assessment, the most straightforward and reliable means of evaluating image enhanc... |
Yu_Landmark_Regularization_Ranking_Guided_Super-Net_Training_in_Neural_Architecture_Search_CVPR_2021_paper | Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search | [
"Kaicheng Yu",
"Rene Ranftl",
"Mathieu Salzmann"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yu_Landmark_Regularization_Ranking_Guided_Super-Net_Training_in_Neural_Architecture_Search_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yu_Landmark_Regularization_Ranking_Guided_Super-Net_Training_in_Neural_Architecture_Search_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yu_Landmark_Regularization_Ranking_CVPR_2021_supplemental.pdf | 2104.05309 | cvf | @InProceedings{Yu_2021_CVPR,
author = {Yu, Kaicheng and Ranftl, Rene and Salzmann, Mathieu},
title = {Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures and that of the corresponding shared-weight networks. This viol... |
Liu_Noise-Resistant_Deep_Metric_Learning_With_Ranking-Based_Instance_Selection_CVPR_2021_paper | Noise-Resistant Deep Metric Learning With Ranking-Based Instance Selection | [
"Chang Liu",
"Han Yu",
"Boyang Li",
"Zhiqi Shen",
"Zhanning Gao",
"Peiran Ren",
"Xuansong Xie",
"Lizhen Cui",
"Chunyan Miao"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liu_Noise-Resistant_Deep_Metric_Learning_With_Ranking-Based_Instance_Selection_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Noise-Resistant_Deep_Metric_Learning_With_Ranking-Based_Instance_Selection_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Liu_Noise-Resistant_Deep_Metric_CVPR_2021_supplemental.pdf | 2103.16047 | cvf | @InProceedings{Liu_2021_CVPR,
author = {Liu, Chang and Yu, Han and Li, Boyang and Shen, Zhiqi and Gao, Zhanning and Ren, Peiran and Xie, Xuansong and Cui, Lizhen and Miao, Chunyan},
title = {Noise-Resistant Deep Metric Learning With Ranking-Based Instance Selection},
booktitle = {Proceedings of the I... | The existence of noisy labels in real-world data negatively impacts the performance of deep learning models. Although much research effort has been devoted to improving robustness to noisy labels in classification tasks, the problem of noisy labels in deep metric learning (DML) remains open. In this paper, we propose a... |
Germain_Neural_Reprojection_Error_Merging_Feature_Learning_and_Camera_Pose_Estimation_CVPR_2021_paper | Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation | [
"Hugo Germain",
"Vincent Lepetit",
"Guillaume Bourmaud"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Germain_Neural_Reprojection_Error_Merging_Feature_Learning_and_Camera_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Germain_Neural_Reprojection_Error_Merging_Feature_Learning_and_Camera_Pose_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Germain_Neural_Reprojection_Error_CVPR_2021_supplemental.pdf | 2103.07153 | cvf | @InProceedings{Germain_2021_CVPR,
author = {Germain, Hugo and Lepetit, Vincent and Bourmaud, Guillaume},
title = {Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
... | Absolute camera pose estimation is usually addressed by sequentially solving two distinct subproblems: First a feature matching problem that seeks to establish putative 2D-3D correspondences, and then a Perspective-n-Point problem that minimizes, w.r.t. the camera pose, the sum of so-called Reprojection Errors (RE). We... |
George_Cross_Modal_Focal_Loss_for_RGBD_Face_Anti-Spoofing_CVPR_2021_paper | Cross Modal Focal Loss for RGBD Face Anti-Spoofing | [
"Anjith George",
"Sebastien Marcel"
] | https://openaccess.thecvf.com/content/CVPR2021/html/George_Cross_Modal_Focal_Loss_for_RGBD_Face_Anti-Spoofing_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/George_Cross_Modal_Focal_Loss_for_RGBD_Face_Anti-Spoofing_CVPR_2021_paper.pdf | null | 2103.00948 | cvf | @InProceedings{George_2021_CVPR,
author = {George, Anjith and Marcel, Sebastien},
title = {Cross Modal Focal Loss for RGBD Face Anti-Spoofing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
p... | Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the literature for presentation attack detection (PAD) fails in generalizing to unseen attacks. In recent years, multi-channel methods have been proposed to im... |
Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper | StickyPillars: Robust and Efficient Feature Matching on Point Clouds Using Graph Neural Networks | [
"Kai Fischer",
"Martin Simon",
"Florian Olsner",
"Stefan Milz",
"Horst-Michael Gross",
"Patrick Mader"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.pdf | null | 2002.03983 | title_snapshot | @InProceedings{Fischer_2021_CVPR,
author = {Fischer, Kai and Simon, Martin and Olsner, Florian and Milz, Stefan and Gross, Horst-Michael and Mader, Patrick},
title = {StickyPillars: Robust and Efficient Feature Matching on Point Clouds Using Graph Neural Networks},
booktitle = {Proceedings of the IEE... | Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initialization, insufficient overlap or in the presence of dynamic objects. Modern deep learning based registration approaches present much bette... |
Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper | HoHoNet: 360 Indoor Holistic Understanding With Latent Horizontal Features | [
"Cheng Sun",
"Min Sun",
"Hwann-Tzong Chen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_HoHoNet_360_Indoor_Holistic_Understanding_With_Latent_Horizontal_Features_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Sun_HoHoNet_360_Indoor_CVPR_2021_supplemental.pdf | 2011.11498 | cvf | @InProceedings{Sun_2021_CVPR,
author = {Sun, Cheng and Sun, Min and Chen, Hwann-Tzong},
title = {HoHoNet: 360 Indoor Holistic Understanding With Latent Horizontal Features},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},... | We present HoHoNet, a versatile and efficient framework for holistic understanding of an indoor 360-degree panorama using a Latent Horizontal Feature (LHFeat). The compact LHFeat flattens the features along the vertical direction and has shown success in modeling per-column modality for room layout reconstruction. HoHo... |
Yan_Online_Learning_of_a_Probabilistic_and_Adaptive_Scene_Representation_CVPR_2021_paper | Online Learning of a Probabilistic and Adaptive Scene Representation | [
"Zike Yan",
"Xin Wang",
"Hongbin Zha"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yan_Online_Learning_of_a_Probabilistic_and_Adaptive_Scene_Representation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yan_Online_Learning_of_a_Probabilistic_and_Adaptive_Scene_Representation_CVPR_2021_paper.pdf | null | 2103.16832 | cvf | @InProceedings{Yan_2021_CVPR,
author = {Yan, Zike and Wang, Xin and Zha, Hongbin},
title = {Online Learning of a Probabilistic and Adaptive Scene Representation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year ... | Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action. In this paper, we represent the scene with a Bayesian nonparametric mixture model, seamlessly describing per-point occupancy status with a continuous probability density function.... |
Liang_Domain_Adaptation_With_Auxiliary_Target_Domain-Oriented_Classifier_CVPR_2021_paper | Domain Adaptation With Auxiliary Target Domain-Oriented Classifier | [
"Jian Liang",
"Dapeng Hu",
"Jiashi Feng"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Liang_Domain_Adaptation_With_Auxiliary_Target_Domain-Oriented_Classifier_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Liang_Domain_Adaptation_With_Auxiliary_Target_Domain-Oriented_Classifier_CVPR_2021_paper.pdf | null | 2007.04171 | cvf | @InProceedings{Liang_2021_CVPR,
author = {Liang, Jian and Hu, Dapeng and Feng, Jiashi},
title = {Domain Adaptation With Auxiliary Target Domain-Oriented Classifier},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | Domain adaptation (DA) aims to transfer knowledge from a label-rich but heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and attracts considerable attention. Different from previous methods focusing on learning domain-invariant feature representations, some recent methods present gene... |
Yin_Learning_To_Recover_3D_Scene_Shape_From_a_Single_Image_CVPR_2021_paper | Learning To Recover 3D Scene Shape From a Single Image | [
"Wei Yin",
"Jianming Zhang",
"Oliver Wang",
"Simon Niklaus",
"Long Mai",
"Simon Chen",
"Chunhua Shen"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yin_Learning_To_Recover_3D_Scene_Shape_From_a_Single_Image_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yin_Learning_To_Recover_3D_Scene_Shape_From_a_Single_Image_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yin_Learning_To_Recover_CVPR_2021_supplemental.pdf | 2012.09365 | cvf | @InProceedings{Yin_2021_CVPR,
author = {Yin, Wei and Zhang, Jianming and Wang, Oliver and Niklaus, Simon and Mai, Long and Chen, Simon and Shen, Chunhua},
title = {Learning To Recover 3D Scene Shape From a Single Image},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Patte... | Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length.... |
Li_Neural_Scene_Flow_Fields_for_Space-Time_View_Synthesis_of_Dynamic_CVPR_2021_paper | Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes | [
"Zhengqi Li",
"Simon Niklaus",
"Noah Snavely",
"Oliver Wang"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Li_Neural_Scene_Flow_Fields_for_Space-Time_View_Synthesis_of_Dynamic_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Neural_Scene_Flow_Fields_for_Space-Time_View_Synthesis_of_Dynamic_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Li_Neural_Scene_Flow_CVPR_2021_supplemental.pdf | 2011.13084 | cvf | @InProceedings{Li_2021_CVPR,
author = {Li, Zhengqi and Niklaus, Simon and Snavely, Noah and Wang, Oliver},
title = {Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
mo... | We present a method to perform novel view and time synthesis of dynamic scenes, requiring only a monocular video with known camera poses as input. To do this, we introduce Neural Scene Flow Fields, a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D s... |
Chen_FS-Net_Fast_Shape-Based_Network_for_Category-Level_6D_Object_Pose_Estimation_CVPR_2021_paper | FS-Net: Fast Shape-Based Network for Category-Level 6D Object Pose Estimation With Decoupled Rotation Mechanism | [
"Wei Chen",
"Xi Jia",
"Hyung Jin Chang",
"Jinming Duan",
"Linlin Shen",
"Ales Leonardis"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Chen_FS-Net_Fast_Shape-Based_Network_for_Category-Level_6D_Object_Pose_Estimation_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_FS-Net_Fast_Shape-Based_Network_for_Category-Level_6D_Object_Pose_Estimation_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Chen_FS-Net_Fast_Shape-Based_CVPR_2021_supplemental.pdf | 2103.07054 | title_snapshot | @InProceedings{Chen_2021_CVPR,
author = {Chen, Wei and Jia, Xi and Chang, Hyung Jin and Duan, Jinming and Shen, Linlin and Leonardis, Ales},
title = {FS-Net: Fast Shape-Based Network for Category-Level 6D Object Pose Estimation With Decoupled Rotation Mechanism},
booktitle = {Proceedings of the IEEE/... | In this paper, we focus on category-level 6D pose and size estimation from a monocular RGB-D image. Previous methods suffer from inefficient category-level pose feature extraction, which leads to low accuracy and inference speed. To tackle this problem, we propose a fast shape-based network (FS-Net) with efficient cate... |
Schmidtke_Unsupervised_Human_Pose_Estimation_Through_Transforming_Shape_Templates_CVPR_2021_paper | Unsupervised Human Pose Estimation Through Transforming Shape Templates | [
"Luca Schmidtke",
"Athanasios Vlontzos",
"Simon Ellershaw",
"Anna Lukens",
"Tomoki Arichi",
"Bernhard Kainz"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Schmidtke_Unsupervised_Human_Pose_Estimation_Through_Transforming_Shape_Templates_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Schmidtke_Unsupervised_Human_Pose_Estimation_Through_Transforming_Shape_Templates_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Schmidtke_Unsupervised_Human_Pose_CVPR_2021_supplemental.zip | 2105.04154 | cvf | @InProceedings{Schmidtke_2021_CVPR,
author = {Schmidtke, Luca and Vlontzos, Athanasios and Ellershaw, Simon and Lukens, Anna and Arichi, Tomoki and Kainz, Bernhard},
title = {Unsupervised Human Pose Estimation Through Transforming Shape Templates},
booktitle = {Proceedings of the IEEE/CVF Conference ... | Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has be... |
Wang_Improving_OCR-Based_Image_Captioning_by_Incorporating_Geometrical_Relationship_CVPR_2021_paper | Improving OCR-Based Image Captioning by Incorporating Geometrical Relationship | [
"Jing Wang",
"Jinhui Tang",
"Mingkun Yang",
"Xiang Bai",
"Jiebo Luo"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Improving_OCR-Based_Image_Captioning_by_Incorporating_Geometrical_Relationship_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Improving_OCR-Based_Image_Captioning_by_Incorporating_Geometrical_Relationship_CVPR_2021_paper.pdf | null | null | null | @InProceedings{Wang_2021_CVPR,
author = {Wang, Jing and Tang, Jinhui and Yang, Mingkun and Bai, Xiang and Luo, Jiebo},
title = {Improving OCR-Based Image Captioning by Incorporating Geometrical Relationship},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti... | OCR-based image captioning aims to automatically describe images based on all the visual entities (both visual objects and scene text) in images. Compared with conventional image captioning, the reasoning of scene text is required for OCR-based image captioning since the generated descriptions often contain multiple OC... |
Yao_Cross-Iteration_Batch_Normalization_CVPR_2021_paper | Cross-Iteration Batch Normalization | [
"Zhuliang Yao",
"Yue Cao",
"Shuxin Zheng",
"Gao Huang",
"Stephen Lin"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yao_Cross-Iteration_Batch_Normalization_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yao_Cross-Iteration_Batch_Normalization_CVPR_2021_paper.pdf | https://openaccess.thecvf.com/content/CVPR2021/supplemental/Yao_Cross-Iteration_Batch_Normalization_CVPR_2021_supplemental.pdf | 2002.05712 | cvf | @InProceedings{Yao_2021_CVPR,
author = {Yao, Zhuliang and Cao, Yue and Zheng, Shuxin and Huang, Gao and Lin, Stephen},
title = {Cross-Iteration Batch Normalization},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
yea... | A well-known issue of Batch Normalization is its significantly reduced effectiveness in the case of small mini-batch sizes. When a mini-batch contains few examples, the statistics upon which the normalization is defined cannot be reliably estimated from it during a training iteration. To address this problem, we presen... |
Yuan_Multimodal_Contrastive_Training_for_Visual_Representation_Learning_CVPR_2021_paper | Multimodal Contrastive Training for Visual Representation Learning | [
"Xin Yuan",
"Zhe Lin",
"Jason Kuen",
"Jianming Zhang",
"Yilin Wang",
"Michael Maire",
"Ajinkya Kale",
"Baldo Faieta"
] | https://openaccess.thecvf.com/content/CVPR2021/html/Yuan_Multimodal_Contrastive_Training_for_Visual_Representation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Multimodal_Contrastive_Training_for_Visual_Representation_Learning_CVPR_2021_paper.pdf | null | 2104.12836 | cvf | @InProceedings{Yuan_2021_CVPR,
author = {Yuan, Xin and Lin, Zhe and Kuen, Jason and Zhang, Jianming and Wang, Yilin and Maire, Michael and Kale, Ajinkya and Faieta, Baldo},
title = {Multimodal Contrastive Training for Visual Representation Learning},
booktitle = {Proceedings of the IEEE/CVF Conferenc... | We develop an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives. Unlike existing visual pre-training methods, which solve a proxy prediction task in a single domain, our method exploits intrinsic data propertie... |
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