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Yu-Gang Jiang
Professor School of Computer Science Fudan University Shanghai ygj AT fudan.edu.cn |
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My research is in the areas of multimedia content analysis and computer vision. I lead the BigVid Lab, conducting research on all aspects of extracting high-level information from big video data, such as video event recognition, object/scene recognition and large-scale visual search. I participate regularly in international benchmark competitions. At the annual U.S. NIST TREC video retrieval evaluation (TRECVID), I have designed a few best-performing systems (among many submissions worldwide) in 2008 video concept detection task and 2010 multimedia event detection task. Recent techniques developed in my group have produced top video content recognition accuracies in various challenges like TRECVID and MediaEval.My publications and citations on Google Scholar can be found here. |
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[02/2017] Our work on video content recognition with regularized DNN has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence.
[06/2016] Our work on image search with hash codes received the 2016 IEEE Trans. on Multimedia Prize Paper Award Honorable Mention. [02/2016] Due to many requests, researchers interested in getting the videos of the CCV dataset can fill up this form and send to me for a link. [02/2015] We have released FCVID, one of the largest public Web video datasets with manual annotations (91,223 videos, 239 categories). |
dataset / code
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FCVID: Fudan-Columbia Video Dataset 91,223 Web videos annotated manually according to 239 categories |
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VCDB: a large-scale database for partial copy detection in videos ECCV 2014 paper |
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Dataset for predicting video emotions AAAI 2014 paper |
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Dataset for predicting video interestingness AAAI 2013 paper |
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Part-level attributes for visual recognition (source code) ECCV 2012 paper |
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CCV: a benchmark dataset for consumer video analysis Note: To obtain a copy of the videos in CCV, please fill up this form and send to me for a link. |
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Domain adaptive semantic diffusion (DASD) for context-based visual annotation refinement ICCV 2009 paper | source code |
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VIREO-374: keypoint-based LSCOM semantic concept detectors
see who has used it CU-VIREO374: fusing Columbia374 and VIREO374 for large scale semantic concept detection see who has used it |
Selected other recent projects
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Query-adaptive image search with hash codes
IEEE TMM 2013 paper |
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Violent scene detection in movies |
Mailing Address: Room 413-5, Computer Science Building, 825 Zhangheng Road, Pudong, Shanghai 201203, China. Tel: +86-21-51355532
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