Profs. Trevor Darrell and Alyosha Efros; trevor,email@example.com
Dr. Katerina Fragkiadaki; firstname.lastname@example.org
Fall 2015CCN 26876
LOCATION AND TIME: Newton room, Room 730 Sutardja Dai Hall, Friday 10-12am
This course covers computer vision and machine learning techniques for object and category recognition, as well as recognition of human activity from video streams. 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. The advent of standardized datasets and evaluation regimes has spurred considerable innovation in this arena, with performance on benchmark evaluations increasing dramatically in recent years. 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, and NIPS conferences.
Prerequisite: Active research effort on related topic. Permission of instructor.