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检索条件"机构=Department of Computer Vision and Image Understanding"
39 条 记 录,以下是1-10 订阅
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Vec2Face-v2: Unveil Human Faces from their Blackbox Features via Attention-based Network in Face Recognition
arXiv
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arXiv 2022年
作者: Truong, Thanh-Dat Duong, Chi Nhan Le, Ngan Savvides, Marios Luu, Khoa The Computer Vision and Image Understanding Lab University of Arkansas United States The Department of Computer Science and Software Engineering Concordia University Canada Carnegie Mellon University United States
In this work, we investigate the problem of face reconstruction given a facial feature representation extracted from a blackbox face recognition engine. Indeed, it is a very challenging problem in practice due to the ... 详细信息
来源: 评论
Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challenge Report
Lens-to-Lens Bokeh Effect Transformation. NTIRE 2023 Challen...
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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023
作者: Conde, Marcos V. Kolmet, Manuel Seizinger, Tim Bishop, Tom E. Timofte, Radu Kong, Xiangyu Zhang, Dafeng Wu, Jinlong Wang, Fan Peng, Juewen Pan, Zhiyu Liu, Chengxin Luo, Xianrui Sun, Huiqiang Shen, Liao Cao, Zhiguo Xian, Ke Liu, Chaowei Chen, Zigeng Yang, Xingyi Liu, Songhua Jing, Yongcheng Mi, Michael Bi Wang, Xinchao Yang, Zhihao Lian, Wenyi Lai, Siyuan Zhang, Haichuan Hoang, Trung Yazdani, Amirsaeed Monga, Vishal Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhao, Yuxuan Chen, Baoliang Xu, Yiqing Niu, Jixiang Computer Vision Lab CAIDAS IFI University of Würzburg Germany Glass Imaging Inc. China Huazhong University of Science and Technology China Nanyang Technological University Singapore National University of Singapore Singapore University of Sydney Australia Huawei Uppsala University Sweden Department of Electrical Engineering Pennsylvania State University United States Department of Information Technology Uppsala University Sweden Key Laboratory of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi'an China North China University of Technology China
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge. Recent advancements of mobile photography a... 详细信息
来源: 评论
Deep Learning Methods for Ship Classification: From Visible to Infrared images  5
Deep Learning Methods for Ship Classification: From Visible ...
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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023
作者: Liu, Tianci Qin, Hengjia Zhan, Zhuo Liu, Yunpeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Liaoning Province Shenyang110027 China
Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize oth... 详细信息
来源: 评论
NTIRE 2023 image Shadow Removal Challenge Report
NTIRE 2023 Image Shadow Removal Challenge Report
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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023
作者: Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China
This work reviews the results of the NTIRE 2023 Challenge on image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consist... 详细信息
来源: 评论
Deep Learning Methods for Ship Classification: From Visible to Infrared images
Deep Learning Methods for Ship Classification: From Visible ...
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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on
作者: Tianci Liu Hengjia Qin Zhuo Zhan Yunpeng Liu Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Laboratory of Image Understanding and Computer Vision Shenyang Liaoning Province China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Shenyang Liaoning Province China
Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize oth...
来源: 评论
Completely self-supervised crowd counting via distribution matching
arXiv
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arXiv 2020年
作者: Sam, Deepak Babu Agarwalla, Abhinav Joseph, Jimmy Sindagi, Vishwanath A. Babu, R. Venkatesh Patel, Vishal M. Video Analytics Lab Department of Computational and Data Sciences Indian Institute of Science Bangalore India Vision & Image Understanding Lab Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States
Dense crowd counting is a challenging task that demands millions of head annotations for training models. Though existing self-supervised approaches could learn good representations, they require some labeled data to ... 详细信息
来源: 评论
Defining quantum neural networks via quantum time evolution
arXiv
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arXiv 2019年
作者: Dendukuri, Aditya Keeling, Blake Fereidouni, Arash Burbridge, Joshua Luu, Khoa Churchill, Hugh Computer Vision and Image Understanding Lab Computer Science and Computer Engineering Department Department of Physics University of Arkansas FayettevilleAR72701 United States Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR72701 United States
This work presents a novel fundamental algorithm for for defining and training Neural Networks in Quantum Information based on time evolution and the Hamiltonian. Classical Neural Network algorithms (ANN) are computat... 详细信息
来源: 评论
Fast Flow Reconstruction via Robust Invertible n × n Convolution
arXiv
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arXiv 2019年
作者: Truong, Thanh-Dat Luu, Khoa Duong, Chi Nhan Le, Ngan Tran, Minh-Triet Computer Vision and Image Understanding Lab Department of Computer Science and Computer Engineering University of Arkansas Arkansas United States Department of Computer Science and Software Engineering Concordia University Quebec Canada Faculty of Information Technology University of Science VNU-HCM Ho Chi Minh Viet Nam
Flow-based generative models have recently become one of the most efficient approaches to model data generation. Indeed, they are constructed with a sequence of invertible and tractable transformations. Glow first int... 详细信息
来源: 评论
image processing in quantum computers
arXiv
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arXiv 2018年
作者: Dendukuri, Aditya Luu, Khoa Computer Vision and Image Understanding Lab Computer Science and Computer Engineering Department University of Arkansas FayettevilleAR72701 United States
Quantum image Processing (QIP) is an exciting new field showing a lot of promise as a powerful addition to the arsenal of image Processing techniques. Representing image pixel by pixel using classical information requ... 详细信息
来源: 评论
Adversarial learning of structure-aware fully convolutional networks for landmark localization
arXiv
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arXiv 2017年
作者: Chen, Yu Shen, Chunhua Chen, Hao Wei, Xiu-Shen Liu, Lingqiao Yang, Jian Key Lab of Intelligent Perception and Systems High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology School of Computer Science University of Adelaide Australian Centre for Robotic Vision Department of Computer Science and Technology Nanjing University
Landmark/pose estimation in single monocular images has received much effort in computer vision due to its important applications. It remains a challenging task when input images come with severe occlusions caused by,... 详细信息
来源: 评论