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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4911-4920 订阅
排序:
Seeing Out of tHe bOx: End-to-End Pre-training for vision-Language Representation Learning
Seeing Out of tHe bOx: End-to-End Pre-training for Vision-La...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Huang, Zhicheng Zeng, Zhaoyang Huang, Yupan Liu, Bei Fu, Dongmei Fu, Jianlong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Beijing Engn Res Ctr Ind Spectrum Imaging Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China
We study joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. State-of-the-art appr... 详细信息
来源: 评论
Neuralizer: General Neuroimage Analysis without Re-Training
Neuralizer: General Neuroimage Analysis without Re-Training
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Czolbe, Steffen Dalca, Adrian V. Univ Copenhagen Copenhagen Denmark MGH Copenhagen Denmark MIT Cambridge MA USA Harvard Med Sch MGH Boston MA USA
Neuroimage processing tasks like segmentation, reconstruction, and registration are central to the study of neuroscience. Robust deep learning strategies and architectures used to solve these tasks are often similar. ... 详细信息
来源: 评论
Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement
Learning Semantic-Aware Knowledge Guidance for Low-Light Ima...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wu, Yuhui Pan, Chen Wang, Guoqing Yang, Yang Wei, Jiwei Li, Chongyi Shen, Heng Tao Univ Elect Sci & Technol China Ctr Future Media Chengdu Peoples R China Nanyang Technol Univ S Lab Singapore Singapore
Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking in... 详细信息
来源: 评论
TransMix: Attend to Mix for vision Transformers
TransMix: Attend to Mix for Vision Transformers
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Chen, Jie-Neng Sun, Shuyang He, Ju Torr, Philip Yuille, Alan Bai, Song Johns Hopkins Univ Baltimore MD 21218 USA Univ Oxford Oxford England ByteDance Inc Beijing Peoples R China
Mixup-based augmentation has been found to be effective for generalizing models during training, especially for vision Transformers (ViTs) since they can easily overfit. However, previous mixup-based methods have an u... 详细信息
来源: 评论
SHViT: Single-Head vision Transformer with Memory Efficient Macro Design
SHViT: Single-Head Vision Transformer with Memory Efficient ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Yun, Seokju Ro, Youngmin Univ Seoul Machine Intelligence Lab Seoul South Korea
Recently, efficient vision Transformers have shown great performance with low latency on resource-constrained devices. Conventionally, they use 4x4 patch embeddings and a 4-stage structure at the macro level, while ut... 详细信息
来源: 评论
Analytical Modeling of Vanishing Points and Curves in Catadioptric Cameras  31
Analytical Modeling of Vanishing Points and Curves in Catadi...
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31st ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Miraldo, Pedro Eiras, Francisco Ramalingam, Srikumar Inst Super Tecn Lisbon Portugal Univ Oxford Oxford England Univ Utah Salt Lake City UT 84112 USA
Vanishing points and vanishing lines are classical geometrical concepts in perspective cameras that have a lineage dating back to 3 centuries. A vanishing point is a point on the image plane where parallel lines in 3D... 详细信息
来源: 评论
Indescribable Multi-modal Spatial Evaluator
Indescribable Multi-modal Spatial Evaluator
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Kong, Lingke Qi, X. Sharon Shen, Qijin Wang, Jiacheng Zhang, Jingyi Hu, Yanle Zhou, Qichao Manteia Tech Xiamen Peoples R China Univ Calif Los Angeles Los Angeles CA USA Fuzhou Univ Fuzhou Peoples R China Xiamen Univ Xiamen Peoples R China Mayo Clin Arizona Phoenix AZ 85054 USA
Multi-modal image registration spatially aligns two images with different distributions. One of its major challenges is that images acquired from different imaging machines have different imaging distributions, making... 详细信息
来源: 评论
Self-Supervised Visibility Learning for Novel View Synthesis
Self-Supervised Visibility Learning for Novel View Synthesis
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Shi, Yujiao Li, Hongdong Yu, Xin Australian Natl Univ Canberra ACT Australia ACRV Canberra ACT Australia Univ Technol Sydney Sydney NSW Australia
We address the problem of novel view synthesis (NVS) from a few sparse source view images. Conventional image-based rendering methods estimate scene geometry and synthesize novel views in two separate steps. However, ... 详细信息
来源: 评论
Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning  32
Object-aware Aggregation with Bidirectional Temporal Graph f...
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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Junchao Peng, Yuxin Peking Univ Inst Comp Sci & Technol Beijing 100871 Peoples R China
Video captioning aims to automatically generate natural language descriptions of video content, which has drawn a lot of attention recent years. Generating accurate and fine-grained captions needs to not only understa... 详细信息
来源: 评论
AEGNN: Asynchronous Event-based Graph Neural Networks
AEGNN: Asynchronous Event-based Graph Neural Networks
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Schaefer, Simon Gehrig, Daniel Scaramuzza, Davide Univ Zurich Dept Informat Zurich Switzerland Univ Zurich Dept Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland
The best performing learning algorithms devised for event cameras work by first converting events into dense representations that are then processed using standard CNNs. However, these steps discard both the sparsity ... 详细信息
来源: 评论