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检索条件"机构=Multimedia Computing and Computer Vision Lab."
73 条 记 录,以下是11-20 订阅
排序:
Addressing data bias problems for chest X-ray image report generation
arXiv
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arXiv 2019年
作者: Harzig, Philipp Chen, Yan-Ying Chen, Francine Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg Germany FX Palo Alto Laboratory 3174 Porter Drive Palo AltoCA United States
Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bia... 详细信息
来源: 评论
Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming
arXiv
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arXiv 2018年
作者: Einfalt, Moritz Zecha, Dan Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Germany
In this paper we consider the problem of human pose estimation in real-world videos of swimmers. Swimming channels allow filming swimmers simultaneously above and below the water surface with a single stationary camer... 详细信息
来源: 评论
Pose estimation for deriving kinematic parameters of competitive swimmers
Pose estimation for deriving kinematic parameters of competi...
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computer vision Applications in Sports 2017
作者: Zecha, Dan Eggert, Christian Lienhart, Rainer Multimedia Computing and Computer Vision Lab. Augsburg University Germany
In the field of competitive swimming a quantitative evaluation of kinematic parameters is a valuable tool for coaches but also a lab.r intensive task. We present a system which is able to automate the extraction of ma... 详细信息
来源: 评论
Multimodal image captioning for marketing analysis
arXiv
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arXiv 2018年
作者: Harzig, Philipp Brehm, Stephan Lienhart, Rainer Kaiser, Carolin Schallner, René Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg86159 Germany GfK Verein Nuremberg90419 Germany
Automatically captioning images with natural language sentences is an important research topic. State of the art models are able to produce human-like sentences. These models typically describe the depicted scene as a... 详细信息
来源: 评论
NTIRE 2020 Challenge on Image and Video Deblurring
arXiv
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arXiv 2020年
作者: Seungjun, Nah Sanghyun, Son Radu, Timofte Kyoung Mu, Lee Tseng, Yu Xu, Yu-Syuan Chiang, Cheng-Ming Tsai, Yi-Min Brehm, Stephan Scherer, Sebastian Xu, Dejia Chu, Yihao Sun, Qingyan Jiang, Jiaqin Duan, Lunhao Yao, Jian Purohit, Kuldeep Suin, Maitreya Rajagopalan, A.N. Ito, Yuichi Hrishikesh, P.S. Puthussery, Densen Akhil, K.A. Jiji, C.V. Kim, Guisik Deepa, P.L. Xiong, Zhiwei Huang, Jie Liu, Dong Kim, Sangmin Nam, Hyungjoon Kim, Jisu Jeong, Jechang Huang, Shihua Fan, Yuchen Yu, Jiahui Yu, Haichao Huang, Thomas S. Zhou, Ya Li, Xin Liu, Sen Chen, Zhibo Dutta, Saikat Das, Sourya Dipta Garg, Shivam Sprague, Daniel Patel, Bhrij Huck, Thomas Department of ECE ASRI SNU Korea Republic of Computer Vision Lab ETH Zurich Switzerland MediaTek Inc University of Augsburg Chair for Multimedia Computing and Computer Vision Lab Germany Peking University China Beijing University of Posts and Telecommunications China Beijing Jiaotong University China Wuhan University China Indian Institute of Technology Madras India Vermilion College of Engineering Trivandrum India CVML Chung-Ang University Korea Republic of APJ Abdul Kalam Technological University India University of Science and Technology of China China Image Communication Signal Processing Laboratory Hanyang University Korea Republic of Southern University of Science and Technology China University of Illinois at Urbana-Champaign United States CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China China IIT Madra Jadavpur University India University of Texas Austin United States Duke University Computer Science Department United States
Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results... 详细信息
来源: 评论
WIDER face and pedestrian challenge 2018 methods and results
arXiv
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arXiv 2019年
作者: Loy, Chen Change Lin, Dahua Ouyang, Wanli Xiong, Yuanjun Yang, Shuo Huang, Qingqiu Zhou, Dongzhan Xia, Wei Li, Quanquan Luo, Ping Yan, Junjie Wang, Jianfeng Li, Zuoxin Yuan, Ye Li, Boxun Shao, Shuai Yu, Gang Wei, Fangyun Ming, Xiang Chen, Dong Zhang, Shifeng Chi, Cheng Lei, Zhen Li, Stan Z. Zhang, Hongkai Ma, Bingpeng Chang, Hong Shan, Shiguang Chen, Xilin Liu, Wu Zhou, Boyan Li, Huaxiong Cheng, Peng Mei, Tao Kukharenko, Artem Vasenin, Artem Sergievskiy, Nikolay Yang, Hua Li, Liangqi Xu, Qiling Hong, Yuan Chen, Lin Sun, Mingjun Mao, Yirong Luo, Shiying Li, Yongjun Wang, Ruiping Xie, Qiaokang Wu, Ziyang Lu, Lei Liu, Yiheng Zhou, Wengang Nanyang Technological University Singapore Singapore Chinese University of Hong Kong Amazon Web Services University of Sydney SenseTime Megvii Microsoft Research Asia University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Jd Ai Research. Computer Vision and Multimedia Lab NtechLab Shanghai Jiao Tong University University of Science and Technology of China and Iflytek
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It compr... 详细信息
来源: 评论
A closer look: Small object detection in faster R-CNN
A closer look: Small object detection in faster R-CNN
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IEEE International Conference on multimedia and Expo (ICME)
作者: Christian Eggert Stephan Brehm Anton Winschel Dan Zecha Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg
Faster R-CNN is a well-known approach for object detection which combines the generation of region proposals and their classification into a single pipeline. In this paper we apply Faster R-CNN to the task of company ... 详细信息
来源: 评论
End-to-end, single-stream temporal action detection in untrimmed videos  28
End-to-end, single-stream temporal action detection in untri...
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28th British Machine vision Conference, BMVC 2017
作者: Buch, Shyamal Escorcia, Victor Ghanem, Bernard Fei-Fei, Li Niebles, Juan Carlos Stanford Vision and Learning Lab. Dept. of Computer Science Stanford University United States Image and Video Understanding Lab. Visual Computing Center KAUST Saudi Arabia
In this work, we present a new intuitive, end-to-end approach for temporal action detection in untrimmed videos. We introduce our new architecture for Single-Stream Temporal Action Detection (SS-TAD), which effectivel... 详细信息
来源: 评论
Saliency-guided selective magnification for company logo detection
Saliency-guided selective magnification for company logo det...
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International Conference on Pattern Recognition
作者: Christian Eggert Anton Winschel Dan Zecha Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg Germany
Fast R-CNN is a well-known approach to object detection which is generally reported to be robust to scale changes. In this paper we examine the influence of scale within the detection pipeline in the case of company l... 详细信息
来源: 评论
Key-Pose Prediction in Cyclic Human Motion
Key-Pose Prediction in Cyclic Human Motion
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IEEE Workshop on Applications of computer vision (WACV)
作者: Dan Zecha Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg
In this paper we study the problem of estimating inner cyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-pos... 详细信息
来源: 评论