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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"
11753 条 记 录,以下是4711-4720 订阅
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SSN: Learning Sparse Switchable Normalization via SparsestMax  32
SSN: Learning Sparse Switchable Normalization via SparsestMa...
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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Shao, Wenqi Meng, Tianjian Li, Jingyu Zhang, Ruimao Li, Yudian Wang, Xiaogang Luo, Ping Chinese Univ Hong Kong CHUK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Univ Pittsburgh Pittsburgh PA 15260 USA
Normalization methods improve both optimization and generalization of ConvNets. To further boost performance, the recently-proposed switchable normalization (SN) provides a new perspective for deep learning: it learns... 详细信息
来源: 评论
Temporal Driver Action Localization using Action Classification Methods
Temporal Driver Action Localization using Action Classificat...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Alyahya, Munirah Alghannam, Shahad Alhussan, Taghreed Saudi Technol & Secur Comprehens Control Co Riyadh Saudi Arabia
Driver distraction recognition is an essential computer vision task that can play a key role in increasing traffic safety and reducing traffic accidents. In this paper, we propose a temporal driver action localization... 详细信息
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Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Rethinking Architecture Design for Tackling Data Heterogenei...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Qu, Liangqiong Zhou, Yuyin Liang, Paul Pu Xia, Yingda Wang, Feifei Adeli, Ehsan Li Fei-Fei Rubin, Daniel Stanford Univ Stanford CA 94305 USA UC Santa Cruz La Jolla CA USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Johns Hopkins Univ Baltimore MD 21218 USA
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution. Despite recent progress, the... 详细信息
来源: 评论
Category-Aware Transformer Network for Better Human-Object Interaction Detection
Category-Aware Transformer Network for Better Human-Object I...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Dong, Leizhen Li, Zhimin Xu, Kunlun Zhang, Zhijun Yan, Luxin Zhong, Sheng Zou, Xu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Peoples R China
Human-Object Interactions (HOI) detection, which aims to localize a human and a relevant object while recognizing their interaction, is crucial for understanding a still image. Recently, tranformer-based models have s... 详细信息
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Robust and Accurate Superquadric Recovery: a Probabilistic Approach
Robust and Accurate Superquadric Recovery: a Probabilistic A...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Liu, Weixiao Wu, Yuwei Ruan, Sipu Chirikjian, Gregory S. Natl Univ Singapore Singapore Singapore Johns Hopkins Univ Baltimore MD 21218 USA
Interpreting objects with basic geometric primitives has long been studied in computer vision. Among geometric primitives, superquadrics are well known for their ability to represent a wide range of shapes with few pa... 详细信息
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Segment Any Event Streams via Weighted Adaptation of Pivotal Tokens
Segment Any Event Streams via Weighted Adaptation of Pivotal...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Chen, Zhiwen Zhu, Zhiyu Zhang, Yifan Hou, Junhui Shi, Guangming Wu, Jinjian Xidian Univ Xian Shaanxi Peoples R China City Univ Hong Kong Hong Kong Peoples R China Pazhou Lab Guangzhou Peoples R China
In this paper, we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robust and universal object segmentation ... 详细信息
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DarkLight Networks for Action recognition in the Dark
DarkLight Networks for Action Recognition in the Dark
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Chen, Rui Chen, Jiajun Liang, Zixi Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co 101 Waihuan Xi Rd Guangzhou 510006 Guangdong Peoples R China
Human action recognition in the dark is a significant task with various applications, e.g., night surveillance and self-driving at night. However, the lack of video datasets for human actions in the dark hinders its d... 详细信息
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Explaining CLIP's performance disparities on data from blind/low vision users
Explaining CLIP's performance disparities on data from blind...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Massiceti, Daniela Longden, Camilla Slowik, Agnieszka Wills, Samuel Grayson, Martin Morrison, Cecily Microsoft Res Redmond WA 98052 USA World Bank 1818 H St NW Washington DC 20433 USA
Large multi-modal models (LMMs) hold the potential to usher in a new era of automated visual assistance for people who are blind or low vision (BLV). Yet, these models have not been systematically evaluated on data ca... 详细信息
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Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
Templates for 3D Object Pose Estimation Revisited: Generaliz...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Van Nguyen Nguyen Hu, Yinlin Xiao, Yang Salzmann, Mathieu Lepetit, Vincent Univ Gustave Eiffel Ecole Ponts LIGM CNRS Champs Sur Marne France Ecole Polytech Fed Lausanne CVLab Lausanne Switzerland
We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, ... 详细信息
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TransNeXt: Robust Foveal Visual Perception for vision Transformers
TransNeXt: Robust Foveal Visual Perception for Vision Transf...
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ieee/cvf conference on computer vision and pattern recognition (cvpr)
作者: Shi, Dai
Due to the depth degradation effect in residual connections, many efficient vision Transformers models that rely on stacking layers for information exchange often fail to form sufficient information mixing, leading to... 详细信息
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