This workshop will involve learning to identifying visually descriptive text, parsing this text and extracting statistical models, and using these models to 1) learn how people describe the world and 2) build more relevant recognition systems in computer vision. It should be anexciting opportunity to deal with large scale text and image data, be exposed to cutting edge techniques in computer vision, andinteractively develop new strategies on the boundary between NLP and computer vision. Specific types of work will include, data collection, parsing, using Amazon’s Mechanical Turk, building andusing probabilistic models, and work on applications including image parsing, retrieval, and automatic sentence generation from images.
Team Members | |
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Senior Members | |
Alexander Berg | Stony Brook University |
Tamara Berg | Stony Brook University |
Hal Daumé III | University of Maryland |
Graduate Students | |
Amit Goyal | University of Maryland |
Xufeng Han | Stony Brook University |
Margaret Mitchell | University of Aberdeen |
Karl Stratos | Columbia University |
Kota Yamaguchi | Stony Brook University |
Undergraduate Students | |
Jesse Dodge | University of Washington |
Alyssa Mensch | Massachusetts Institute of Technology |
Affiliate Members | |
Yejin Choi | Stony Brook University |
Julia Hockenmaier | University of Illinois at Urbana-Champaign |
Erik Learned-Miller | University of Massachusetts, Amherst |
Alan Qi | Purdue University |