Yu-Gang Jiang

Professor
School of Computer Science
Fudan University
Shanghai

ygj AT fudan.edu.cn
 


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. I am also one of the organizers of the THUMOS Challenge on Large Scale Action Recognition.

My publications and citations on Google Scholar can be found here.


[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] NEW! We have released FCVID, one of the largest public Web video datasets with manual annotations (91,223 videos, 239 categories).
[11/2014] Our paper received best poster award at ACM Multimedia 2014.
[08/2014] The website of ACM ICMR 2015 (June 23-26, Shanghai, China) is online.
[08/2014] We have released VCDB, the largest public dataset with manually annotated real partial video copies.
[08/2014] The workshop of the 2nd THUMOS Action Recognition Challenge will be held in conjunction with ECCV 2014.
[01/2014] The violent scenes detection dataset used in MediaEval 2013 is available online, jointly provided by Technicolor, Fudan, and UIT Vietnam.

dataset / code

FCVID: Fudan-Columbia Video Dataset
91,223 Web videos annotated manually according to 239 categories
VCDB: a large-scale database for partial copy detection in videos
ECCV 2014 paper
Dataset for predicting video emotions
AAAI 2014 paper
Dataset for predicting video interestingness
AAAI 2013 paper
Part-level attributes for visual recognition (source code)
ECCV 2012 paper
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.
Domain adaptive semantic diffusion (DASD) for context-based visual annotation refinement
ICCV 2009 paper | source code
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

Query-adaptive image search with hash codes
IEEE TMM 2013 paper
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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