Trevor Darrell, Alyosha Efros; {trevor,efros} eecs.berkeley.edu
David Fouhey; {dfouhey} eecs.berkeley.edu
Pre-requisite for the course: CS280 graduate computer vision or active research effort on related topic with permission of instructor.
Weekly Schedule
Location and time
Cory 337B
Mondays 10:30 - 12am
Agenda
This course covers computer vision and machine learning techniques for object and activity recognition, as well as new emerging directions and learning techniques. Emphasis will be placed on recent techniques based on layered perceptual representation learning, a.k.a. "deep" learning. Recognition of individual objects or activities (the coffee cup on your desk, a particular chair in your office, a video of you riding your bike) or generic categories (any cup, chair, or cycling event) is an essential capability for a variety of robotics and multimedia applications. This course reviews methods from the recent literature (past 6-9 months) that have achieved success on such challenge problems, and may also consider the techniques needed for real-time interactive application on robots or mobile devices, e.g. domestic service robots or mobile phones that can retrieve information about objects in the environment based on visual observation. This class is based exclusively on readings from the recent literature, including those appearing at the CVPR, ICCV, ECCV, ICML, NIPS, and ICLR conferences.