Graduate Student / Postdoc Seminar

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Learning Good Features for Understanding Video Data

Speaker: Graham Taylor

Location: Warren Weaver Hall 1302

Date: Friday, March 26, 2010, 1:30 p.m.

Synopsis:

Currently the best performing methods for recognizing human activities in video are based on engineered descriptors with explicit local geometric cues and other heuristics. I will talk about an alternative method that uses unsupervised learning to extract both perceptually relevant low-level features that are sensitive to motion and sparse mid-level features that capture longer-term temporal effects. We apply our method to recognizing actions in Hollywood movies. This is joint work with Rob Fergus, Chris Bregler and Yann LeCun.