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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"
30976 条 记 录,以下是4701-4710 订阅
排序:
Coupled Dictionary and Feature Space Learning with Applications to Cross-Domain Image Synthesis and recognition
Coupled Dictionary and Feature Space Learning with Applicati...
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IEEE International conference on computer vision (ICCV)
作者: Huang, De-An Wang, Yu-Chiang Frank Acad Sinica Res Ctr Informat Technol Innovat Taipei 115 Taiwan
Cross-domain image synthesis and recognition are typically considered as two distinct tasks in the areas of computer vision and pattern recognition. Therefore, it is not clear whether approaches addressing one task ca... 详细信息
来源: 评论
Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation
Look Closer to Segment Better: Boundary Patch Refinement for...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Tang, Chufeng Chen, Hang Li, Xiao Li, Jianmin Zhang, Zhaoxiang Hu, Xiaolin Tsinghua Univ THU Bosch JCML Ctr State Key Lab Intelligent Technol & Syst Dept Comp Sci & TechnolInst AIBNRist Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China
Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature ... 详细信息
来源: 评论
Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations  32
Devil is in the Edges: Learning Semantic Boundaries from Noi...
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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Acuna, David Kar, Amlan Fidler, Sanja NVIDIA Toronto ON Canada Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada
We tackle the problem of semantic boundary prediction, which aims to identify pixels that belong to object(class) boundaries. We notice that relevant datasets consist of a significant level of label noise, reflecting ... 详细信息
来源: 评论
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation
Boundary IoU: Improving Object-Centric Image Segmentation Ev...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Cheng, Bowen Girshick, Ross Dollar, Piotr Berg, Alexander C. Kirillov, Alexander UIUC Urbana IL 61820 USA Facebook AI Res FAIR Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA
We present Boundary IoU (Intersection-over-Union), a new segmentation evaluation measure focused on boundary quality. We perform an extensive analysis across different error types and object sizes and show that Bounda... 详细信息
来源: 评论
Towards Total Recall in Industrial Anomaly Detection
Towards Total Recall in Industrial Anomaly Detection
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Roth, Karsten Pemula, Latha Zepeda, Joaquin Scholkopf, Bernhard Brox, Thomas Gehler, Peter Univ Tubingen Tubingen Germany Amazon AWS Seattle WA USA
Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defecti... 详细信息
来源: 评论
ACRE: Abstract Causal REasoning Beyond Covariation
ACRE: Abstract Causal REasoning Beyond Covariation
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Chi Jia, Baoxiong Edmonds, Mark Zhu, Song-Chun Zhu, Yixin UCLA Ctr Vis Cognit Learning & Auton Los Angeles CA 90095 USA
Causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and... 详细信息
来源: 评论
Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
Contextualized Spatio-Temporal Contrastive Learning with Sel...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Yuan, Liangzhe Qian, Rui Cui, Yin Gong, Boqing Schroff, Florian Yang, Ming-Hsuan Adam, Hartwig Liu, Ting Google Res Mountain View CA 94043 USA Cornell Univ Ithaca NY 14853 USA Google Mountain View CA 94043 USA
Modern self:supervised learning algorithms typically enforce persistency of instance representations across views. While being very effective on learning holistic image and video representations, such an objective bec... 详细信息
来源: 评论
Unsupervised Person Image Generation with Semantic Parsing Transformation  32
Unsupervised Person Image Generation with Semantic Parsing T...
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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Song, Sijie Zhang, Wei Liu, Jiaying Mei, Tao Peking Univ Inst Comp Sci & Technol Beijing Peoples R China JD AI Res Beijing Peoples R China
In this paper, we address unsupervised pose-guided person image generation, which is known challenging due to non-rigid deformation. Unlike previous methods learning a rock-hard direct mapping between human bodies, we... 详细信息
来源: 评论
Continual Stereo Matching of Continuous Driving Scenes with Growing Architecture
Continual Stereo Matching of Continuous Driving Scenes with ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Chenghao Tian, Kun Fan, Bin Meng, Gaofeng Zhang, Zhaoxiang Pan, Chunhong Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China HK Inst Sci & Innovat CAS Ctr Artificial Intelligence & Robot Hong Kong Peoples R China
The deep stereo models have achieved state-of-the-art performance on driving scenes, but they suffer from severe performance degradation when tested on unseen scenes. Although recent work has narrowed this performance... 详细信息
来源: 评论
Using Unknown Occluders to Recover Hidden Scenes  32
Using Unknown Occluders to Recover Hidden Scenes
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Yedidia, Adam B. Baradad, Manel Thrampoulidis, Christos Freeman, William T. Wornell, Gregory W. MIT Cambridge MA 02139 USA UC Santa Barbara Santa Barbara CA USA Google Res Mountain View CA USA
We consider the challenging problem of inferring a hidden moving scene from faint shadows cast on a diffuse surface. Recent work in passive non-line-of-sight (NLoS) imaging has shown that the presence of occluding obj... 详细信息
来源: 评论