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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition Workshops"
23198 条 记 录,以下是4941-4950 订阅
排序:
Deep Video Matting via Spatio-Temporal Alignment and Aggregation
Deep Video Matting via Spatio-Temporal Alignment and Aggrega...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Sun, Yanan Wang, Guanzhi Gu, Qiao Tang, Chi-Keung Tai, Yu-Wing HKUST Daejeon South Korea Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Kuaishou Technol Seoul South Korea
Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning te... 详细信息
来源: 评论
All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation  30
All You Need is Beyond a Good Init: Exploring Better Solutio...
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30th ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Xie, Di Xiong, Jiang Pu, Shiliang Hikvis Res Inst Hangzhou Zhejiang Peoples R China
Deep neural network is difficult to train and this predicament becomes worse as the depth increases. The essence of this problem exists in the magnitude of backpropagated errors that will result in gradient vanishing ... 详细信息
来源: 评论
DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering
DIVeR: Real-time and Accurate Neural Radiance Fields with De...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wu, Liwen Lee, Jae Yong Bhattad, Anand Wang, Yu-Xiong Forsyth, David Univ Illinois Champaign IL 61820 USA
DIVeR builds on the key ideas of NeRF and its variants-density models and volume rendering - to learn 3D object models that can be rendered realistically from small numbers of images. In contrast to all previous NeRF ... 详细信息
来源: 评论
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation
Generalizing to the Open World: Deep Visual Odometry with On...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Shunkai Wu, Xin Cao, Yingdian Zha, Hongbin Peking Univ Sch EECS Key Lab Machine Percept MOE PKU SenseTime Machine Vis Joint Lab Beijing Peoples R China
Despite learning-based visual odometry (VO) has shown impressive results in recent years, the pretrained networks may easily collapse in unseen environments. The large domain gap between training and testing data make... 详细信息
来源: 评论
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Semantic Segmentation
Segment-Fusion: Hierarchical Context Fusion for Robust 3D Se...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Thyagharajan, Anirud Ummenhofer, Benjamin Laddha, Prashant Omer, Om Ji Subramoney, Sreenivas Intel Labs Processor Architecture Res Lab India Bangalore Karnataka India Intel Labs Autonomous Agents Lab Germany Bangalore Karnataka India
3D semantic segmentation is a fundamental building block for several scene understanding applications such as autonomous driving, robotics and AR/VR. Several state-of-the-art semantic segmentation models suffer from t... 详细信息
来源: 评论
Real-time Hyperspectral Imaging in Hardware via Trained Metasurface Encoders
Real-time Hyperspectral Imaging in Hardware via Trained Meta...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Makarenko, Maksim Burguete-Lopez, Arturo Wang, Qizhou Getman, Fedor Giancola, Silvio Ghanem, Bernard Fratalocchi, Andrea King Abdullah Univ Sci & Technol KAUST Thuwal 239556900 Saudi Arabia
Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision. State-of-the-art implementations of snapshot hyp... 详细信息
来源: 评论
Low-Shot Learning from Imaginary Data  31
Low-Shot Learning from Imaginary Data
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31st ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Yu-Xiong Girshick, Ross Hebert, Martial Hariharan, Bharath FAIR Santa Monica CA 90401 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Cornell Univ Ithaca NY 14853 USA
Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new co... 详细信息
来源: 评论
Deep Progressive Reinforcement Learning for Skeleton-based Action recognition  31
Deep Progressive Reinforcement Learning for Skeleton-based A...
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31st ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Tang, Yansong Tian, Yi Lu, Jiwen Li, Peiyang Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ State Key Lab Intelligent Technol & Syst Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China
In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in seque... 详细信息
来源: 评论
Exploring Unlabeled Faces for Novel Attribute Discovery
Exploring Unlabeled Faces for Novel Attribute Discovery
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Bahng, Hyojin Chung, Sunghyo Yoo, Seungjoo Choo, Jaegul Korea Univ Seoul South Korea Kakao Corp Jeju Si South Korea Korea Adv Inst Sci & Technol Daejeon South Korea
Despite remarkable success in unpaired image-to-image translation, existing approaches still require a large amount of labeled images. This is a bottleneck against their realworld applications;in practice, a model tra... 详细信息
来源: 评论
End-to-End Learnable Geometric vision by Backpropagating PnP Optimization
End-to-End Learnable Geometric Vision by Backpropagating PnP...
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ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Chen, Bo Parra, Alvaro Cao, Jiewei Li, Nan Chin, Tat-Jun Univ Adelaide Adelaide SA Australia Shenzhen Univ Shenzhen Peoples R China
Deep networks excel in learning patterns from large amounts of data. On the other hand, many geometric vision tasks are specified as optimization problems. To seamlessly combine deep learning and geometric vision, it ... 详细信息
来源: 评论