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检索条件"机构=Key Laboratory of Image Processing and Pattern Recognition"
844 条 记 录,以下是301-310 订阅
AIM 2020 Challenge on image Extreme Inpainting  16th
AIM 2020 Challenge on Image Extreme Inpainting
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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020
作者: Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India
This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semanti... 详细信息
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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING
arXiv
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arXiv 2022年
作者: Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China
With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets ... 详细信息
来源: 评论
Corrigendum to Intrusion detection based on improved density peak clustering for imbalanced data on sensor-cloud systems Journal of Systems Architecture volume 118 (2021) 102212
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Journal of Systems Architecture 2021年 119卷
作者: Ming Yan Yewang Chen Xiaoliang Hu Dongdong Cheng Yi Chen Jixiang Du The College of Computer Science and Technology Huaqiao University Xiamen China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China College of Big Data and Intelligent Engineering Yangtze Normal University Chongqing China
来源: 评论
Motion Object Detection Method based on Real-time Background Update under Complex Environment  2
Motion Object Detection Method based on Real-time Background...
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2017 IEEE 第2届先进信息技术,电子与自动化控制国际会议(IAEAC 2017)
作者: Zixiao Pan Mei Wang School of Automation Wuhan University of Technology Laboratory of Image Processing and Pattern Recognition Yantai Vocational College
For the fast requirement of motion object detection under complex environment, a background subtraction motion object detection method based on real-time background update is presented in this paper.
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Enlightening the relationship between distribution and regression fitting  2nd
Enlightening the relationship between distribution and regre...
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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017
作者: Yu, Hang Yin, Qian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China
Statistical distribution fitting and regression fitting are both classic methods to model data. There are slight connections and differences between them, as a result they outperform each other in different cases. A a... 详细信息
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Weighting features before applying machine learning methods to pulsar search  2nd
Weighting features before applying machine learning methods ...
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2nd IFIP TC 12 International Conference on Intelligence Science, ICIS 2017
作者: Wang, Dayang Yin, Qian Wang, Hongfeng Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing100875 China
In recent years, different Artificial Intelligence methods have been applied to pulsar search, such as Artificial Neural Network method, PEACE Sorting Algorithm, Real-time Classification method. In this paper, Weighti... 详细信息
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Pseudoinverse Learning Algorithom for Fast Sparse Autoencoder Training
Pseudoinverse Learning Algorithom for Fast Sparse Autoencode...
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IEEE Congress on Evolutionary Computation
作者: Bingxin Xu Ping Guo Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China
Sparse autoencoder is one approach to automatically learn features from unlabeled data and received significant attention during the development of deep neural networks. However, the learning algorithm of sparse autoe... 详细信息
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Incremental transformer with deliberation decoder for document grounded conversations
arXiv
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arXiv 2019年
作者: Li, Zekang Niu, Cheng Meng, Fandong Feng, Yang Li, Qian Zhou, Jie Dian Group School of Electronic Information and Communications Huazhong University of Science and Technology Pattern Recognition Center WeChat AI Tencent Inc China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Engineering Northeastern University China
Document Grounded Conversations is a task to generate dialogue responses when chatting about the content of a given document. Obviously, document knowledge plays a critical role in Document Grounded Conversations, whi... 详细信息
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An improved convex programming model for the inverse problem in intensity-modulated radiation therapy
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International Journal of Performability Engineering 2018年 第5期14卷 871-884页
作者: Lan, Yihua Zhang, Xingang Zhang, Jianyang Wang, Yang Hung, Chih-Cheng School of Computer and Information Technology Nanyang Normal University Nanyang473061 China Institute of Image Processing and Pattern Recognition Nanyang Normal University Nanyang473061 China Radiology Department Central Hospital of Nanyang Nanyang473061 China Laboratory for Machine Vision and Security Research College of Computing and Software Engineering Kennesaw State University - Marietta Campus 1100 South Marietta Parkway MariettaGA30067-2896 United States
Intensity modulated radiation therapy technology (IMRT) is one of the main approaches in cancer treatment because it can guarantee the killing of cancer cells while optimally protecting normal tissue from complication... 详细信息
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Face recognition with Convolutional Neural Networks and subspace learning  2
Face Recognition with Convolutional Neural Networks and subs...
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2nd International Conference on image, Vision and Computing, ICIVC 2017
作者: Wan, Lihong Liu, Na Huo, Hong Tao, Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China
Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN ... 详细信息
来源: 评论