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检索条件"机构=Institute of Image Processing and Pattern recognition"
1341 条 记 录,以下是91-100 订阅
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An Self-supervised Learning Self-attention Based Method for Defects Classification on PCB Surface images  2
An Self-supervised Learning Self-attention Based Method for ...
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2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021
作者: Zhou, Lijie Ling, Xufeng Zhu, Shan Sun, Zheng Yang, Jie AI School Tianhua College Shanghai Normal University Shanghai China ASIC Motor Corporation Limited China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China
Automatic detection of PCB defects become a difficult work in electronics industry, with the rapid development of Integrated Circuit. A good detection method can effectively improve production efficiency and reduce th... 详细信息
来源: 评论
OAS-Net: Occlusion Aware Sampling Network for Accurate Optical Flow
OAS-Net: Occlusion Aware Sampling Network for Accurate Optic...
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IEEE International Conference on Acoustics, Speech and Signal processing
作者: Lingtong Kong Xiaohang Yang Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China
Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm, where a key pr... 详细信息
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LTSP: Long-term slice propagation for accurate airway segmentation
arXiv
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arXiv 2022年
作者: Wu, Yangqian Zhang, Minghui Yu, Weihao Zheng, Hao Xu, Jiasheng Gu, Yun Institute Of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute Of Image Processing And Pattern Recognition Shanghai Jiao Tong University Shanghai China
Purpose: Bronchoscopic intervention is a widely-used clinical technique for pulmonary diseases, which requires an accurate and topological complete airway map for its localization and guidance. The airway map could be... 详细信息
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Unsupervised motion representation enhanced network for action recognition
arXiv
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arXiv 2021年
作者: Yang, Xiaohang Kong, Lingtong Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China
Learning reliable motion representation between consecutive frames, such as optical flow, has proven to have great promotion to video understanding. However, the TV-L1 method, an effective optical flow solver, is time... 详细信息
来源: 评论
Unsupervised Motion Representation Enhanced Network for Action recognition
Unsupervised Motion Representation Enhanced Network for Acti...
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IEEE International Conference on Acoustics, Speech and Signal processing
作者: Xiaohang Yang Lingtong Kong Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China
Learning reliable motion representation between consecutive frames, such as optical flow, has proven to have great promotion to video understanding. However, the TV-L1 method, an effective optical flow solver, is time... 详细信息
来源: 评论
UniGNN: A unified framework for graph and hypergraph neural networks
arXiv
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arXiv 2021年
作者: Huang, Jing Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China
Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. Despite the success of Graph Neura... 详细信息
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OAS-Net: Occlusion aware sampling network for accurate optical flow
arXiv
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arXiv 2021年
作者: Kong, Lingtong Yang, Xiaohang Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China
Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm, where a key pr... 详细信息
来源: 评论
Learn What You Need in Personalized Federated Learning
arXiv
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arXiv 2024年
作者: Lv, Kexin Ye, Rui Huang, Xiaolin Yang, Jie Chen, Siheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China Shanghai AI Laboratory Shanghai200232 China
Personalized federated learning aims to address data heterogeneity across local clients in federated learning. However, current methods blindly incorporate either full model parameters or predefined partial parameters... 详细信息
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Generating Cartoon images from Face Photos with Cycle-Consistent Adversarial Networks
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Computers, Materials & Continua 2021年 第11期69卷 2733-2747页
作者: Tao Zhang Zhanjie Zhang Wenjing Jia Xiangjian He Jie Yang School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi214000China Key Laboratory of Artificial Intelligence Jiangsu214000China The Global Big Data Technologies Centre University of Technology SydneyUltimoNSW2007Australia The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai201100China
The generative adversarial network(GAN)is first proposed in 2014,and this kind of network model is machine learning systems that can learn to measure a given distribution of data,one of the most important applications... 详细信息
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Online LiDAR-Camera Extrinsic Parameters Self-checking
arXiv
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arXiv 2022年
作者: Wei, Pengjin Yan, Guohang Li, Yikang Fang, Kun Yang, Jie Liu, Wei The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China The Autonomous Driving Group Shanghai AI Laboratory China
With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This calibration task is multi-modal, wher... 详细信息
来源: 评论