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检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"
5506 条 记 录,以下是581-590 订阅
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QuadricSLAM: Dual Quadrics as SLAM Landmarks  31
QuadricSLAM: Dual Quadrics as SLAM Landmarks
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Nicholson, Lachlan Milford, Michael Sunderhauf, Niko Queensland Univ Technol ARC Ctr Excellence Robot Vis Brisbane Qld Australia
Research in Simultaneous Localization And Mapping (SLAM) is increasingly moving towards richer world representations involving objects and high level features that enable a semantic model of the world for robots, pote... 详细信息
来源: 评论
Multiple People Tracking using Body and Joint Detections  32
Multiple People Tracking using Body and Joint Detections
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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Henschel, Roberto Zou, Yunzhe Rosenhahn, Bodo Leibniz Univ Hannover Hannover Germany
Most multiple people tracking systems compute trajectories based on the tracking-by-detection paradigm. Consequently, the performance depends to a large extent on the quality of the employed input detections. However,... 详细信息
来源: 评论
M2SGD: Learning to Learn ImportantWeights
M<SUP>2</SUP>SGD: Learning to Learn ImportantWeights
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Kuo, Nicholas I-Hsien Harandi, Mehrtash Fourrier, Nicolas Walder, Christian Ferraro, Gabriela Suominen, Hanna Australian Natl Univ RSCS Canberra ACT Australia Monash Univ ECSE Clayton Vic Australia CSIRO Data61 Canberra ACT Australia Vole Univ Leonard de Vinci Paris France Univ Turku Dept Future Technol Turku Finland
Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their hand-c... 详细信息
来源: 评论
Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy from Face Images  32
Color-Theoretic Experiments to Understand Unequal Gender Cla...
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32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Muthukumar, Vidya Pedapati, Tejaswini Ratha, Nalini Sattigeri, Prasanna Wu, Chai-Wah Kingsbury, Brian Kumar, Abhishek Thomas, Samuel Mojsilovic, Aleksandra Varshney, Kush R. IBM Res Zurich Switzerland Univ Calif Berkeley Berkeley CA 94720 USA Google Brain Mountain View CA USA
Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Accuracy on dark-skinned females is sign... 详细信息
来源: 评论
End-to-End Learned Image Compression with Augmented Normalizing Flows
End-to-End Learned Image Compression with Augmented Normaliz...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Ho, Yung-Han Chan, Chih-Chun Peng, Wen-Hsiao Hang, Hsueh-Ming Natl Chiao Tung Univ Comp Sci Dept Hsinchu Taiwan Natl Chiao Tung Univ Elect Engn Dept Hsinchu Taiwan Natl Chiao Tung Univ Pervas AI Res PAIR Labs Hsinchu Taiwan
This paper presents a new attempt at using augmented normalizing flows (ANF) for lossy image compression. ANF is a specific type of normalizing flow models that augment the input with an independent noise, allowing a ... 详细信息
来源: 评论
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
Video Class Agnostic Segmentation Benchmark for Autonomous D...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Siam, Mennatullah Kendall, Alex Jagersand, Martin Univ Alberta Edmonton AB Canada Wayve London England
Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical robotics applications, especially au... 详细信息
来源: 评论
Inaccuracy of State-Action Value Function For Non-Optimal Actions in Adversarially Trained Deep Neural Policies
Inaccuracy of State-Action Value Function For Non-Optimal Ac...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Korkmaz, Ezgi KTH Royal Inst Technol Stockholm Sweden
The introduction of deep neural networks as function approximator for the state-action value function has led to the creation of a new research area for self-learning systems that explore policies from high dimensiona... 详细信息
来源: 评论
Asymmetric Information Distillation Network for Lightweight Super Resolution
Asymmetric Information Distillation Network for Lightweight ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zong, Zhikai Zha, Lin Jiang, Jiande Liu, Xiaoxiao Qingdao Hiimage Technol Co Ltd Hisense Visual Technol Co Ltd Qingdao Peoples R China
The purpose of this paper is to design a lightweight network to achieve image super resolution performance equivalent to SRResNet. We design an asymmetric information distillation block (AIDB) with distillation inform... 详细信息
来源: 评论
GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation
GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Potamias, Rolandos Alexandros Neofytou, Alexandros Bintsi, Kyriaki Margarita Zafeiriou, Stefanos Imperial Coll London London England OCM Digital Media Athens Greece
Geodesic paths and distances are among the most popular intrinsic properties of 3D surfaces. Traditionally, geodesic paths on discrete polygon surfaces were computed using shortest path algorithms, such as Dijkstra. H... 详细信息
来源: 评论
Spacing Loss for Discovering Novel Categories
Spacing Loss for Discovering Novel Categories
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Joseph, K. J. Paul, Sujoy Aggarwal, Gaurav Biswas, Soma Rai, Piyush Han, Kai Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Google Res Bengaluru India Indian Inst Sci Bengaluru India Lndian Inst Technol Kanpur Kanpur Uttar Pradesh India Univ Hong Kong Hong Kong Peoples R China
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this ... 详细信息
来源: 评论