Episode 51: Research and Tooling for Computer Vision Systems with Jason Corso
Show Notes(2:13) Jason went over his experience studying Computer Science at Loyola College in Baltimore for undergraduate, where he got an early exposure to academic research in image registration.(4:31) Jason described his graduate school experience at John Hopkins University, where he completed his Ph.D. on “Techniques for Vision-Based Human-Computer Interaction” that proposed the Visual Interaction Cues paradigm.(9:31) During his time as a Post-Doc Fellow at UCLA, Jason helped develop automatic segmentation and recognition techniques for brain tumors to improve the accuracy of diagnosis and treatment accuracy(14:27) From 2007 to 2014, Jason was a professor in the Computer Science and Engineering department at SUNY-Buffalo. He covered the content of two graduate-level courses on Bayesian Vision and Intro to Pattern Recognition that he taught.(18:20) On the topic of metric learning, Jason proposed an approach to data analysis and modeling for computer vision called "Active Clustering."(21:35) On the topic of image understanding, Jason created Generalized Image Understanding - a project that examined a unified methodology that integrates low-, mid-, and high-level elements for visual inference (equivalent to image captioning today).(24:51) On the topic of video understanding, Jason worked on ISTARE: Intelligent Spatio-Temporal Activity Reasoning Engine, whose objective is to represent, learn, recognize, and reason over activities in persistent surveillance videos.(27:46) Jason dissected Action Bank - a high-level representation of activity in video, which comprises of many individual action detectors sampled broadly in semantic space and viewpoint space.(35:30) Jason unpacked LIBSVX - a library of super voxel and video segmentation methods coupled with a principled evaluation benchmark based on quantitative 3D criteria for good super voxels.(40:06) Jason gave an overview of AI research activities at the University of Michigan, where he was a professor of Electrical Engineering and Computer Science from 2014 to 2020.(41:09) Jason covered the problems and projects in his graduate-level courses on Foundations of Computer Vision and Advanced Topics in Computer Vision at Michigan.(44:56) Jason went over his recent research on video captioning and video description.(47:03) Jason described his exciting software called BubbleNets, which chooses the best video frame for a human to annotate.(51:44) Jason shared anecdotes of Voxel51's inception and key takeaways that he has learned.(01:05:25) Jason talked about Voxel51's Physical Distancing Index that tracks the coronavirus global pandemic's impact on social behavior.(01:07:47) Jason discussed his exciting new chapter as the new director of the Stevens Institute for Artificial Intelligence.(01:11:28) Jason identified the differences and similarities between being a professor and being a founder.(01:14:55) Jason gave his advice to individuals who want to make a dent in AI research.(01:16:14) Jason mentioned the trends in computer vision research that he is most excited about at the moment.(01:17:23) Closing segment.His Contact InfoWikipediaGoogle ScholarWebsiteTwitterLinkedInHis Recommended ResourcesBubblenets: Video Object Segmentation for Computer VisionVoxel51's FiftyOne Open-Sourced LibraryJeff Siskind (Professor at Purdue University)CJ Taylor (Professor at the University of Pennsylvania)Kristen Grauman (Professor at the University of Austin)"An Introduction to Mathematical Statistics"