Dear all, Our next AI seminar is scheduled for Friday, December 6th. 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<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Foregonstate.zoom.us%2Fs%2F98357211915&data=05%7C02%7Cai%40engr.oregonstate.edu%7Cc23a749777db4fea48ed08dd132188ff%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638687756939108557%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=wU9pd%2FfEeq%2FCC45n%2FLOl2FdILfDsEVA393Uc1XRfSCk%3D&reserved=0> 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. If you would like to meet with the speaker one-on-one, please email tadepall@oregonstate.edu<mailto:tadepall@oregonstate.edu> by Tuesday. For future AI seminars, please visit: https://engineering.oregonstate.edu/EECS/research/AI-seminars<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fengineering.oregonstate.edu%2FEECS%2Fresearch%2FAI-seminars&data=05%7C02%7Cai%40engr.oregonstate.edu%7Cc23a749777db4fea48ed08dd132188ff%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638687756939108557%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=2lfhuy7mgRKTsen2ihHYhX3EfpmbONNFgoJMPVVFjfU%3D&reserved=0>. Best, Christian Abou Mrad Graduate Student Oregon State University