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检索条件"任意字段=2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006"
6858 条 记 录,以下是291-300 订阅
排序:
Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels
Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Se...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Kalluri, Tarun Chandraker, Manmohan Univ Calif San Diego La Jolla CA 92093 USA
Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. However, a more general scenario is when the source and target datasets correspond to no... 详细信息
来源: 评论
S2F2: Single-Stage Flow Forecasting for Future Multiple Trajectories Prediction
S2F2: Single-Stage Flow Forecasting for Future Multiple Traj...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Chen, Yu-Wen Yang, Hsuan-Kung Chiu, Chu-Chi Lee, Chun-Yi Natl Tsing Hua Univ Dept Comp Sci Elsa Lab Hsinchu Taiwan
In this work, we present a single-stage framework, named S2F2, for forecasting multiple human trajectories from raw video images by predicting future optical flows. S2F2 differs from the previous two-stage approaches ... 详细信息
来源: 评论
Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion
Improving Multi-Target Multi-Camera Tracking by Track Refine...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Specker, Andreas Florin, Lucas Cormier, Mickael Beyerer, Juergen Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany
Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic... 详细信息
来源: 评论
Unstructured Object Matching using Co-Salient Region Segmentation
Unstructured Object Matching using Co-Salient Region Segment...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Stoian, Ioana-Sabina Sandu, Ionut-Catalin Voinea, Daniel Popa, Alin-Ionut Amazon Bucharest Romania
Unstructured object matching is a less-explored and very challenging topic in the scientific literature. This includes matching scenarios where the context, appearance and the geometrical integrity of the objects to b... 详细信息
来源: 评论
Self-Supervised Learning of Pose-Informed Latents
Self-Supervised Learning of Pose-Informed Latents
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Jean, Raphael St-Charles, Pierre-Luc Pirk, Soren Brodeur, Simon Menya Solut Sherbrooke PQ Canada Mila Montreal PQ Canada AMLRT Montreal PQ Canada Google Res Mountain View CA USA
Siamese network architectures trained for self-supervised instance recognition can learn powerful visual representations that are useful in various tasks. Many such approaches maximize the similarity between represent... 详细信息
来源: 评论
recognition of Freely Selected Keypoints on Human Limbs
Recognition of Freely Selected Keypoints on Human Limbs
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Ludwig, Katja Kienzle, Daniel Lienhart, Rainer Univ Augsburg Machine Learning & Comp Vis Lab Augsburg Germany
Nearly all Human Pose Estimation (HPE) datasets consist of a fixed set of keypoints. Standard HPE models trained on such datasets can only detect these keypoints. If more points are desired, they have to be manually a... 详细信息
来源: 评论
PersonGONE: Image Inpainting for Automated Checkout Solution
PersonGONE: Image Inpainting for Automated Checkout Solution
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Bartl, Vojtech Spanhel, Jakub Herout, Adam Brno Univ Technol Fac Informat Technol GRAPH FIT Bozetechova 1-2 Brno 61266 Czech Republic
In this paper, we present a solution for automatic checkout in a retail store as a part of AI City Challenge 2022. We propose a novel approach that uses the "removal" of unwanted objects - in this case, body... 详细信息
来源: 评论
Unsupervised Change Detection Based on Image Reconstruction Loss
Unsupervised Change Detection Based on Image Reconstruction ...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Noh, Hyeoncheol Ju, Jingi Seo, Minseok Park, Jongchan Choi, Dong-Geol Hanbat Natl Univ Daejeon South Korea SI Analyt Inc Mainz Germany Lunit Inc Seoul South Korea
To train a change detector, bi-temporal images taken at different times in the same area are used. However, collecting labeled bi-temporal images is expensive and time consuming. To solve this problem, various unsuper... 详细信息
来源: 评论
A New Non-central Model for Fisheye Calibration
A New Non-central Model for Fisheye Calibration
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Tezaur, Radka Kumar, Avinash Nestares, Oscar Intel Corp Santa Clara CA 95054 USA
A new non-central model suitable for calibrating fisheye cameras is proposed. It is a direct extension of the popular central model developed by Scaramuzza et al., used by Matlab computer vision Toolbox fisheye calibr... 详细信息
来源: 评论
Ice hockey player identification via transformers and weakly supervised learning
Ice hockey player identification via transformers and weakly...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Vats, Kanav McNally, William Walters, Pascale Clausi, David A. Zelek, John S. Univ Waterloo Syst Design Engn Waterloo ON Canada Stathletes Inc St Catharines ON Canada
Identifying players in video is a foundational step in computer vision-based sports analytics. Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recogni... 详细信息
来源: 评论