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检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition"
23240 条 记 录,以下是4951-4960 订阅
排序:
Omni Aggregation Networks for Lightweight Image Super-Resolution
Omni Aggregation Networks for Lightweight Image Super-Resolu...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wang, Hang Chen, Xuanhong Ni, Bingbing Liu, Yutian Liu, Jinfan Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China
While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF)... 详细信息
来源: 评论
FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer
FlatFormer: Flattened Window Attention for Efficient Point C...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Liu, Zhijian Yang, Xinyu Tang, Haotian Yang, Shang Han, Song MIT Cambridge MA 02139 USA Shanghai Jiao Tong Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China
Transformer, as an alternative to CNN, has been proven effective in many modalities (e.g., texts and images). For 3D point cloud transformers, existing efforts focus primarily on pushing their accuracy to the state-of... 详细信息
来源: 评论
Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics
Improving Semi-Supervised Domain Adaptation Using Effective ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Singh, Anurag Doraiswamy, Naren Takamuku, Sawa Bhalerao, Megh Dutta, Titir Biswas, Soma Chepuri, Aditya Vengatesan, Balasubramanian Natori, Naotake Indian Inst Sci Bangalore Karnataka India Aisin Corp Toyota Japan Aisin Automot Haryana Pvt Ltd Bangalore Karnataka India
Recently, semi-supervised domain adaptation (SSDA) approaches have shown impressive performance for the domain adaptation task. They effectively utilize few labeled target samples along with the unlabeled data to acco... 详细信息
来源: 评论
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
Motion-aware Contrastive Video Representation Learning via F...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Ding, Shuangrui Li, Maomao Yang, Tianyu Qian, Rui Xu, Haohang Chen, Qingyi Wang, Jue Xiong, Hongkai Shanghai Jiao Tong Univ Shanghai Peoples R China Tencent AI Lab Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Michigan Ann Arbor MI 48109 USA
In light of the success of contrastive learning in the image domain, current self-supervised video representation learning methods usually employ contrastive loss to facilitate video representation learning. When naiv... 详细信息
来源: 评论
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data
Best of Both Worlds: Multimodal Contrastive Learning with Ta...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Hager, Paul Menten, Martin J. Rueckert, Daniel Tech Univ Munich Munich Germany Klinikum Rechts Der Isar Munich Germany Imperial Coll London London England
Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and sca... 详细信息
来源: 评论
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
P3Depth: Monocular Depth Estimation with a Piecewise Planari...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Patil, Vaishakh Sakaridis, Christos Liniger, Alexander Gool, Luc Van Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven PSI Leuven Belgium
Monocular depth estimation is vital for scene understanding and downstream tasks. We focus on the supervised setup, in which ground-truth depth is available only at training time. Based on knowledge about the high reg... 详细信息
来源: 评论
Versatile Multi-Modal Pre-Training for Human-Centric Perception
Versatile Multi-Modal Pre-Training for Human-Centric Percept...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Hong, Fangzhou Pan, Liang Cai, Zhongang Liu, Ziwei Nanyang Technol Univ S Lab Singapore Singapore SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China
Human-centric perception plays a vital role in vision and graphics. But their data annotations are prohibitively expensive. Therefore, it is desirable to have a versatile pretrain model that serves as a foundation for... 详细信息
来源: 评论
Boosting Co-teaching with Compression Regularization for Label Noise
Boosting Co-teaching with Compression Regularization for Lab...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Chen, Yingyi Shen, Xi Hu, Shell Xu Suykens, Johan A. K. Katholieke Univ Leuven ESAT STADIUS Leuven Belgium UPE Ecole Ponts LIGM UMR 8049 Champs Sur Marne France Upload AI LLC Houston TX USA
In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout [22]. We find that Nested Dropout [22], t... 详细信息
来源: 评论
TopNet: Structural Point Cloud Decoder  32
TopNet: Structural Point Cloud Decoder
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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Tchapmi, Lyne P. Kosaraju, Vineet Rezatofighi, S. Hamid Reid, Ian Savarese, Silvio Stanford Univ Stanford CA 94305 USA Univ Adelaide Adelaide SA Australia
3D point cloud generation is of great use for 3D scene modeling and understanding. Real-world 3D object point clouds can be properly described by a collection of lowlevel and high-level structures such as surfaces, ge... 详细信息
来源: 评论
Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images
Interventional Bag Multi-Instance Learning On Whole-Slide Pa...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Lin, Tiancheng Yu, Zhimiao Hu, Hongyu Xu, Yi Chen, Chang Wen Shanghai Jiao Tong Univ Shanghai Key Lab Digital Media Proc & Transmiss Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China
Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL methods primarily focus on impr... 详细信息
来源: 评论