Steerable Part Models: Hamed Pirsiavash, MIT CSAIL (IVC)
- Starts: 1:00 pm on Monday, February 18, 2013
- Ends: 2:00 pm on Monday, February 18, 2013
Abstract: We describe a method for learning steerable deformable part models. Our models exploit the fact that part templates can be written as linear ﬁlter banks. We demonstrate that one can enforce steerability and separability during learning by applying rank constraints. These constraints are enforced with a coordinate descent learning algorithm, where each step can be solved with an off-the-shelf structured SVM solver. The resulting models are orders of magnitude smaller than their counterparts, greatly simplifying learning and reducing run-time computation. Limiting the degrees of freedom also reduces overﬁtting, which is useful for learning large part vocabularies from limited training data. We learn steerable variants of several state-of-the-art models for object detection, human pose estimation, and facial landmark estimation. Our steerable models are smaller, faster, and often improve performance. Bio: Hamed Pirsiavash is currently a Postdoctoral Associate at MIT, CSAIL working with Prof Antonio Torralba. He completed his PhD in computer science at the University of California Irvine with Prof. Deva Ramanan in 2012. His research interests are in computer vision and machine learning and specifically on action recognition in videos.
- MCS 148