Large-scale detector adaptation and other recent results

In this talk I'll review recent progress towards robust and effective perceptual representation learning.  I'll describe new methods for large-scale detection, whereby robust detectors can be learned from weakly labeled training data, following paradigms of domain adaptation and multiple instance learning.  I'll discuss how such models can be used not only for detection but also for pose prediction and further for effective fine-grained recognition, extending traditional convolutional neural network models to include explicit pose-normalized descriptors. Finally, and time permitting (pardon the pun), I'll review our recent work on anytime recognition, which provides methods that strive to provide the best answer possible, even with a limited (and unknown) time budget.

Prof. Trevor Darrell’s group is co-located at the University of California, Berkeley, and the UCB-affiliated International Computer Science Institute (ICSI), also located in Berkeley, CA. Prof. Darrell is on the faculty of the CS Division of the EECS Department at UCB and is the vision group lead at ICSI.  Darrell’s group develops algorithms for large-scale perceptual learning, including object and activity recognition and detection, for a variety of applications including multimodal interaction with robots and mobile devices. His interests include computer vision, machine learning, computer graphics, and perception-based human computer interfaces. Prof. Darrell was previously on the faculty of the MIT EECS department from 1999-2008, where he directed the Vision Interface Group. He was a member of the research staff at Interval Research Corporation from 1996-1999, and received the S.M., and PhD. degrees from MIT in 1992 and 1996, respectively. He obtained the B.S.E. degree from the University of Pennsylvania in 1988, having started his career in computer vision as an undergraduate researcher in Ruzena Bajcsy's GRASP lab.