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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"
3320 条 记 录,以下是231-240 订阅
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CoVIO: Online Continual Learning for Visual-Inertial Odometry
CoVIO: Online Continual Learning for Visual-Inertial Odometr...
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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023
作者: Vödisch, Niclas Cattaneo, Daniele Burgard, Wolfram Valada, Abhinav University of Freiburg Germany University of Technology Nuremberg Germany
Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains and learning-based vision systems ar... 详细信息
来源: 评论
MobileViG: Graph-Based Sparse Attention for Mobile vision Applications
MobileViG: Graph-Based Sparse Attention for Mobile Vision Ap...
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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023
作者: Munir, Mustafa Avery, William Marculescu, Radu The University of Texas at Austin United States
Traditionally, convolutional neural networks (CNN) and vision transformers (ViT) have dominated computer vision. However, recently proposed vision graph neural networks (ViG) provide a new avenue for exploration. Unfo... 详细信息
来源: 评论
Self-Supervised Video Similarity Learning
Self-Supervised Video Similarity Learning
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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023
作者: Kordopatis-Zilos, Giorgos Tolias, Giorgos Tzelepis, Christos Kompatsiaris, Ioannis Patras, Ioannis Papadopoulos, Symeon Czech Technical University VRG FEE Prague Czech Republic Queen Mary University London United Kingdom Information Technologies Institute CERTH Greece
We introduce S2VS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to have strong transferability on target tasks... 详细信息
来源: 评论
A Data-Driven Approach based on Dynamic Mode Decomposition for Efficient Encoding of Dynamic Light Fields
A Data-Driven Approach based on Dynamic Mode Decomposition f...
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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023
作者: Ravishankar, Joshitha Khaidem, Sally Sharma, Mansi Indian Institute of Technology Madras Department of Electrical Engineering India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India
Dynamic light fields provide a richer, more realistic 3D representation of a moving scene. However, this leads to higher data rates since excess storage and transmission requirements are needed. We propose a novel app... 详细信息
来源: 评论
Robustness of Visual Explanations to Common Data Augmentation Methods
Robustness of Visual Explanations to Common Data Augmentatio...
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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023
作者: Tětková, Lenka Hansen, Lars Kai Technical University of Denmark Department of Applied Mathematics and Computer Science Richard Petersens Plads 321 Lyngby2800 Kgs. Denmark
As the use of deep neural networks continues to grow, understanding their behaviour has become more crucial than ever. Post-hoc explainability methods are a potential solution, but their reliability is being called in... 详细信息
来源: 评论
Towards Detailed Characteristic-Preserving Virtual Try-On
Towards Detailed Characteristic-Preserving Virtual Try-On
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Lee, Sangho Lee, Seoyoung Lee, Joonseok Seoul Natl Univ Seoul South Korea
While virtual try-on has rapidly progressed recently, existing virtual try-on methods still struggle to faithfully represent various details of the clothes when worn. In this paper, we propose a simple yet effective m... 详细信息
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Area Under the ROC Curve Maximization for Metric Learning
Area Under the ROC Curve Maximization for Metric Learning
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Gajic, Bojana Amato, Ariel Baldrich, Ramon van de Weijer, Joost Gatta, Carlo Vintra Inc Barcelona Spain Comp Vis Ctr Barcelona Spain
Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing t... 详细信息
来源: 评论
OutfitGAN: Learning Compatible Items for Generative Fashion Outfits
OutfitGAN: Learning Compatible Items for Generative Fashion ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Moosaei, Maryam Lin, Yusan Akhazhanov, Ablaikhan Chen, Huiyuan Wang, Fei Yang, Hao Visa Res San Francisco CA 94158 USA Univ Calif Los Angeles Los Angeles CA 90024 USA Nazarbayev Univ Astana Kazakhstan
Fashion-on-demand is becoming an important concept for fashion industries. Many attempts have been made to leverage machine learning methods to generate fashion designs tailored to customers' tastes. However, how ... 详细信息
来源: 评论
BinaryViT: Pushing Binary vision Transformers Towards Convolutional Models
BinaryViT: Pushing Binary Vision Transformers Towards Convol...
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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023
作者: Le, Phuoc-Hoan Charles Li, Xinlin Huawei Noah's Ark Lab Montreal Research Center Canada Huawei Noah's Ark Lab Canada
With the increasing popularity and the increasing size of vision transformers (ViTs), there has been an increasing interest in making them more efficient and less computationally costly for deployment on edge devices ... 详细信息
来源: 评论
Anomaly Detection in Autonomous Driving: A Survey
Anomaly Detection in Autonomous Driving: A Survey
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Bogdoll, Daniel Nitsche, Maximilian Zoellner, J. Marius FZI Res Ctr Informat Technol Karlsruhe Germany KIT Karlsruhe Inst Technol Karlsruhe Germany
Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the ... 详细信息
来源: 评论