Talk details:

AI Seminar: From Pixels to Measurements: Understanding the Dynamic World

Speaker: Dr. Adam Harley, Postdoctoral Scholar, Stanford University

Time: 2:00 PM

Location: KEC 1001 and Zoom

Zoom link: https://oregonstate.zoom.us/s/98357211915

Talk Abstract:

In computer vision, “video understanding” typically concerns summarization: tracking the main objects, or describing the main actions. While progress here has been impressive, many practical applications require extracting information which is much more fine-grained. For example, biologists are highly interested in tracking specific key points of organisms in long video recordings. Algorithms for such tasks require the generality and precision of low-level vision methods (e.g., optical flow), but benefit from knowledge about the 3D physical world (e.g., things continue to exist while they are occluded). In this talk, I will present our progress on this crucial space of problems. Our central contribution is to widen the window of “temporal context” used for inference: instead of tracking entities from one frame to the next, we inspect dozens of frames simultaneously, and return an answer that makes sense for the full clip. I will discuss the methods and datasets that we have created to drive progress along these lines, and highlight natural science applications of the work. Finally, I will introduce our ongoing effort to produce a “foundation model” of motion, aiming to deliver reliable arbitrary-granularity tracking for the huge variety of real-world situations where this is required.

Speaker Bio:

Adam Harley is a postdoctoral scholar at Stanford University, working with Leonidas Guibas. He received a Ph.D. in robotics from Carnegie Mellon University, where he worked with Katerina Fragkiadaki. He received his M.S. in Computer Science at Toronto Metropolitan University, working with Kosta Derpanis. Adam is a recipient of the NSERC PGS-D scholarship, and the Toronto Metropolitan University Gold Medal. His research interests lie in Computer Vision and Machine Learning, particularly for 3D understanding and fine-grained tracking.

For future AI seminars, please visit: https://engineering.oregonstate.edu/EECS/research/AI-seminars.

Best,

Christian Abou Mrad 

Graduate Student 

Oregon State University