Overview & Current Syllabus

Prof. Trevor Darrell, Prof. Alyosha Efros; {trevor,efros} eecs.berkeley.edu

Dr. Marcus Rohrbach{rohrbach} eecs.berkeley.edu

Pre-requisite for the course: CS280 graduate computer vision or active research effort on related topic with permission of instructor.

Location and time

Newton room, Room 730 Sutardja Dai Hall, Mondays 10-12am


We will use this google drive folder for planning and drafting reviews. If you don't have premission to view/edit this folder please email Marcus.

Finalized reviews will be published on theberkeleyview which is a public blog.

Please read how we expect participation in this course: https://sites.google.com/site/ucbcs29443/review-pipeline 


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.