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检索条件"机构=Shenzhen Key Laboratory of Robotics and Computer Vision"
493 条 记 录,以下是241-250 订阅
排序:
Dense graph convolutional neural networks on 3D meshes for 3D object segmentation and classification
arXiv
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arXiv 2021年
作者: Tang, Wenming Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence Robotics for Society Shenzhen China School of Computer Science The University of Nottingham United Kingdom
This paper presents new designs of graph convolutional neural networks (GCNs) on 3D meshes for 3D object segmentation and classification. We use the faces of the mesh as basic processing units and represent a 3D mesh ... 详细信息
来源: 评论
Learning to predict context-adaptive convolution for semantic segmentation
arXiv
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arXiv 2020年
作者: Liu, Jianbo He, Junjun Ren, Jimmy S. Qiao, Yu Li, Hongsheng CUHK-SenseTime Joint Laboratory Chinese University of Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SenseTime Research
Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods [34] demonstrate that using global context for re-weighting feature channels c... 详细信息
来源: 评论
Learning dynamical human-joint affinity for 3D pose estimation in videos
arXiv
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arXiv 2021年
作者: Zhang, Junhao Wang, Yali Zhou, Zhipeng Luan, Tianyu Wang, Zhe Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of California Irvine United States Shanghai AI Laboratory Shanghai China
Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. However, it is often built on the fixed human-joint affinity, according to human skeleton. This may reduce adaptation ... 详细信息
来源: 评论
Boundary and entropy-driven adversarial learning for fundus image segmentation
arXiv
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arXiv 2019年
作者: Wang, Shujun Yu, Lequan Li, Kang Yang, Xin Fu, Chi-Wing Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China
Accurate segmentation of the optic disc (OD) and cup (OC) in fundus images from different datasets is critical for glaucoma disease screening. The cross-domain discrepancy (domain shift) hinders the generalization of ... 详细信息
来源: 评论
DegAE: A New Pretraining Paradigm for Low-Level vision
DegAE: A New Pretraining Paradigm for Low-Level Vision
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Conference on computer vision and Pattern Recognition (CVPR)
作者: Yihao Liu Jingwen He Jinjin Gu Xiangtao Kong Yu Qiao Chao Dong Shanghai Artificial Intelligence Laboratory ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences The University of Sydney
Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? Wha...
来源: 评论
Dynamic hand gesture early recognition based on Hidden Semi-Markov Models
Dynamic hand gesture early recognition based on Hidden Semi-...
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IEEE International Conference on robotics and Biomimetics
作者: Qianqian Wang Yuanrong Xu Yen-Lun Chen Yong Wang Xinyu Wu University of Science and Technology of China Shenzhen Key Lab for Computer Vision and Pattern Recognition Chinese Academy of Sciences Dept. Mechanical and Automation Engineering The Chinese University of Hong Kong. Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences
Real-time performance can be greatly improved, if the early recognition is implemented. In this paper, a dynamic hand gesture early recognition system is proposed. The system can recognize the gesture before it is com... 详细信息
来源: 评论
computer vision and Image Understanding
SSRN
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SSRN 2022年
作者: Tang, Wenming Gong, Yuanhao Qiu, Guoping College of Electronics and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science The University of Nottingham United Kingdom
Graph neural networks (GNNs) are ideally suited for mesh denoising. However, existing solutions such as those based on graph convolutional networks (GCNs) are built for a fixed graph thus making them not naturally gen... 详细信息
来源: 评论
Context-transformer: Tackling object confusion for few-shot detection
arXiv
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arXiv 2020年
作者: Yang, Ze Wang, Yali Chen, Xianyu Liu, Jianzhuang Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Huawei Noah’s Ark Lab. SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society
Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e.,... 详细信息
来源: 评论
Incentive-driven Federated Learning in Mobile Edge Networks
Incentive-driven Federated Learning in Mobile Edge Networks
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International Conference on Distributed Computing Systems Workshop
作者: Yanlang Zheng Huan Zhou Liang Zhao Shouzhi Xu Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China
Federated Learning (FL) is proposed as a privacy-preserving distributed learning methodology that can better protect the privacy and reduce communication costs. To stimulate sufficient User Equipments (UEs) to partici...
来源: 评论
SATO: Stable Text-to-Motion Framework  24
SATO: Stable Text-to-Motion Framework
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32nd ACM International Conference on Multimedia, MM 2024
作者: Chen, Wenshuo Xiao, Hongru Zhang, Erhang Hu, Lijie Wang, Lei Liu, Mengyuan Chen, Chen Shandong University Qingdao China Tongji University Shanghai China King Abdullah University of Science and Technology Jeddah Saudi Arabia Australian National University Data61/CSIRO Canberra Australia State Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School Shenzhen China Center for Research in Computer Vision University of Central Florida Orlando United States
Is the Text to Motion model robust? Recent advancements in Text to Motion models primarily stem from more accurate predictions of specific actions. However, the text modality typically relies solely on pre-trained Con... 详细信息
来源: 评论