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20180110-1 周文罡:Pseudo-supervised Deep Learning for Image Search

2017-12-29 11:05| 发布者: 程一-计算所| 查看: 4277| 评论: 0

摘要: 报告嘉宾:周文罡(中国科学技术大学)报告时间:2018年01月10日(星期三)晚20:00(北京时间)报告题目:Pseudo-supervised Deep Learning for Image Search主持人:王楠楠(西安电子科技大学)报告摘要:Recent ye ...



报告题目:Pseudo-supervised Deep Learning for Image Search



Recent years has witnessed the great success of deep learning in a variety of vision tasks. In most cases, deep learning is conducted in a supervised way. As for image search, since the category number of potential objects is difficult to enumerate, it is infeasible to collect the expected training data for training data devoted to image search. As a result, most works on image search simply leverage the activations from pre-trained deep learning model. To this end, we explore deep learning in a pseudo-supervised paradigm and orient it for image retrieval. We approach it from different perspectives and propose three algorithms. Experiments demonstrate the effectiveness and potential of pseudo-supervised deep learning in retrieval task.


[1] Wengang Zhou, Houqiang Li, Jian Sun, and Qi Tian, “Collaborative Index Embedding for Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Feb. 2017.

[2] Min Wang, Wengang Zhou, Qi Tian, and Houqiang Li, “A General Framework for Linear Distance Preserving Hashing,” IEEE Transactions on Image Processing (TIP), vol. 27, no. 2, pp. 907-922, Aug. 2017.

[3] Min Wang, Wengang Zhou, Qi Tian, Zheng-jun Zha, and Houqiang Li, "Linear Distance Preserving Pseudo-Supervised and Unsupervised Hashing," ACM International Conference on Multimedia (MM), pp. 1257-1266, long paper, 1257-1266, 2016.


Dr. Wengang Zhou received his PhD degree in the Department of Electronic Engineering and Information Science (EEIS), University of Science and Technology of China (USTC) in 2011. He is currently an associate professor in USTC. Before that, he worked as a post-doctorate researcher in the Computer Science Department, University of Texas at San Antonio (UTSA). His research interests include multimedia content analysis & retrieval and computer vision. He has published over 80 research papers in journals including IEEE T-PAMI, IEEE T-IP, and IEEE T-MM, and in conferences including IEEE CVPR and ACM Multimedia. He is the recipient of the Best Paper award in the ACM ICMICS 2012, the Excellent Doctoral Dissertation Award of Chinese Academy of Science in 2013, and the Young Elite Scientists Sponsorship Program by CAST 2016. He severs as the reviewer for IEEE T-PAMI, IEEE T-IP, IEEE T-CSVT, IEEE T-MM, CVPR, ACM MM, ICCV, ECCV, etc.






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