Event Location: Gates 7101
Bio: Dinesh Jayaraman is a PhD candidate in Kristen Grauman’s group at UT Austin. His research interests are broadly in visual recognition and machine learning. In the last few years, Dinesh has worked on visual learning and active recognition in embodied agents, unsupervised representation learning from unlabeled video, visual attribute prediction, and zero-shot categorization. During his PhD, he has received the Best Application Paper Award at the Asian Conference on Computer Vision 2016 for work on automatic cinematography, the Samsung PhD Fellowship for 2016-17, a UT Austin Microelectronics and Computer Development Fellowship, and a Graduate Dean’s Prestigious Fellowship Supplement Award for 2016-17. Before beginning graduate school, Dinesh graduated with a bachelor’s degree in electrical engineering from the Indian Institute of Technology Madras (IITM), Chennai, India.

Abstract: Visual recognition methods have made great strides in recent years by exploiting large manually curated and labeled datasets specialized to various tasks. My research focuses on asking: could we do better than this painstakingly manually supervised approach? In particular, could embodied visual agents teach themselves through interaction with and experimentation in their environments?

In this talk, I will present approaches that we have developed to model the learning and performance of visual tasks by agents that have the ability to act and move in their worlds. I will showcase results that indicate that computer vision systems could benefit greatly from action and motion in the world, with continuous self-acquired feedback. In particular, it is possible for embodied visual agents to learn generic image representations from unlabeled video, improve scene and object categorization performance through intelligent exploration, and even learn to direct their cameras to be effective videographers.