CAM Colloquium: Bharath Hariharan (CS, Cornell) - The enduring mysteries of learned visual representations
Frank H. T. Rhodes Hall 655
Object recognition in computer vision has seen a surge of progress in the past half-decade, powered by deep “convolutional neural networks.” One of the most under-appreciated yet surprising findings from this progress is that convolutional networks trained on one task learn internal representations of images that are useful for a completely different task. This kind of “meta”-generalization goes above and beyond what is traditionally explored in machine learning. As such, it opens up several intriguing questions: why does such generalization happen, what properties of the “source” and “target” task affect it, and how can we improve this generalization? In this talk, I will present results from my group as well as the broader computer vision community that has begun to explore these questions.
Bharath Hariharan is an assistant professor in the Department of Computer Science at Cornell University. He came to Cornell after a 2-year stint at Facebook AI Research, before which he did his PhD at UC Berkeley. His interests are in computer vision, specifically on visual recognition: designing machine vision systems that can recognize objects as quickly and effectively as humans do.