16-824 Learning Based Methods in Vision, Fall 2013

Upcoming dates:	August 27: Classes start September 3: Deadline to sign up for papers (as presenter and critic) September 15: Project proposal
Location:	NSH 3002
Time:	Tuesday, Thursday 13:30-14:50
Course Description:	A graduate seminar course in Computer Vision with emphasis on representation and reasoning for large amounts of data (images, videos and associated tags, text, gps-locations etc) toward the ultimate goal of Image Understanding. We will be reading an eclectic mix of classic and recent papers on topics including: Theories of Perception, Mid-level Vision (Grouping, Segmentation, Poselets), Object and Scene Recognition, 3D Scene Understanding, Action Recognition, Contextual Reasoning, Image Parsing, Joint Language and Vision Models, etc. We will be covering a wide range of supervised, semi-supervised and unsupervised approaches for each of the topics above.
Prerequisites:	While there are no formal prerequisites, this course assumes familiarity with computer vision (16-720 or similar) and machine learning (10-601 or similar). If you have not taken courses covering this material, consult with the professor.
Similar Classes:	This course last year (Alexei Efros, CMU, Spring 2012) Visual Recognition (Kristen Grauman, Texas-Austin, Fall 2012) Grounding Object Recognition and Scene Understanding (Antonio Torralba, MIT, Fall 2011) Computer Vision (Ali Farhadi) Visual Scene Understanding (Derek Hoiem, UIUC, Spring 2009) The Cutting Edge of Computer Vision (Fei-Fei Li, Stanford, Spring 2013) Statistical Models for Visual Recognition (Deva Ramanan, UCI, Winter 2009)
Awards:	At the end of the course there will be prizes for each of the following: Best Blog Post Best Reviewer Best Research Proposal Best Project