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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"
31061 条 记 录,以下是4251-4260 订阅
Scaled 360 layouts: Revisiting non-central panoramas
Scaled 360 layouts: Revisiting non-central panoramas
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Berenguel-Baeta, Bruno Bermudez-Cameo, Jesus Guerrero, Jose J. Univ Zaragoza I3A Zaragoza Spain
From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In... 详细信息
来源: 评论
Virtual Fully-Connected Layer: Training a Large-Scale Face recognition Dataset with Limited Computational Resources
Virtual Fully-Connected Layer: Training a Large-Scale Face R...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Pengyu Wang, Biao Zhang, Lei Alibaba Grp Artificial Intelligence Ctr DAMO Acad Hangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China
Recently, deep face recognition has achieved significant progress because of Convolutional Neural Networks (CNNs) and large-scale datasets. However, training CNNs on a large-scale face recognition dataset with limited... 详细信息
来源: 评论
An Empirical Study of Scaling Law for Scene Text recognition
An Empirical Study of Scaling Law for Scene Text Recognition
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conference on computer vision and pattern recognition (CVPR)
作者: Miao Rang Zhenni Bi Chuaniian Liu Yunhe Wang Kai Han Huawei Noah's Ark Lab
The laws of model size, data volume, computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However, the scaling laws in Scene Text recognition (STR) have no... 详细信息
来源: 评论
Towards Robust Classification Model by Counterfactual and Invariant Data Generation
Towards Robust Classification Model by Counterfactual and In...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chang, Chun-Hao Adam, George Alexandru Goldenberg, Anna Univ Toronto Hosp Sick Children Vector Inst Toronto ON Canada
Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions. Spuriousness o... 详细信息
来源: 评论
Capsule Network is Not More Robust than Convolutional Network
Capsule Network is Not More Robust than Convolutional Networ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Gu, Jindong Tresp, Volker Hu, Han Univ Munich Munich Germany Microsoft Res Asia Beijing Peoples R China
The Capsule Network is widely believed to be more robust than Convolutional Networks. However, there are no comprehensive comparisons between these two networks, and it is also unknown which components in the CapsNet ... 详细信息
来源: 评论
Taming Transformers for High-Resolution Image Synthesis
Taming Transformers for High-Resolution Image Synthesis
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Esser, Patrick Rombach, Robin Ommer, Bjoern Heidelberg Univ Heidelberg Collaboratory Image Proc IWR Heidelberg Germany
Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes loc... 详细信息
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MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers
MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Tran...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Huiyu Zhu, Yukun Adam, Hartwig Yuille, Alan Chen, Liang-Chieh Johns Hopkins Univ Baltimore MD 21218 USA Google Res Mountain View CA USA Google Mountain View CA 94043 USA
We present MaX-DeepLab, the first end-to-end model for panoptic segmentation. Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks and hand-designed components, such as box detectio... 详细信息
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All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers
All You Can Embed: Natural Language based Vehicle Retrieval ...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Scribano, Carmelo Sapienza, Davide Franchini, Giorgia Verucchi, Micaela Bertogna, Marko Univ Modena & Reggio Emilia Modena Italy Univ Ferrara Ferrara Italy Univ Parma Parma Italy
Combining Natural Language with vision represents a unique and interesting challenge in the domain of Artificial Intelligence. The AI City Challenge Track 5 for Natural Language-Based Vehicle Retrieval focuses on the ... 详细信息
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Essentials for Class Incremental Learning
Essentials for Class Incremental Learning
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Mittal, Sudhanshu Galesso, Silvio Brox, Thomas Univ Freiburg Freiburg Germany
Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for m... 详细信息
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Compact and Effective Representations for Sketch-based Image Retrieval
Compact and Effective Representations for Sketch-based Image...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Torres, Pablo Saavedra, Jose M. Univ Chile DCC Av Beauchef 851 Santiago Chile Impresee Inc 600 Calif St San Francisco CA USA
Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce searc... 详细信息
来源: 评论