21 Computer Vision News Computer Vision News Deepti Ghadiyaram Deepti Ghadiyaram is an assistant professor at Boston University. Deepti, what is your work about? My students and I focus primarily on understanding how different large scale, small scale language models, vision language models, vision models assimilate information and how they respond to different reasoning tasks. The goal is that, by understanding how these models work, we will be able to build better, more generalizable algorithms. When did you guys start to work on this? I started at BU in 2024, in July of 2024. That's when I recruited students and together all of us have been pursuing this research agenda. Why did you choose this subject? I've been interested in the field of computer vision building models for a very long time. I spent over five years at Facebook and there we've had an opportunity to work on very large scale models. But it was always the failure mode that fascinated me as to how can a model that can do such difficult tasks so well fail or struggle on such benign, slightly tweaked inputs. That as a question has always intrigued me. Probably because it's a probabilistic model and not deterministic or something like that, right? Correct, yeah. Understanding that and resolving those has been something that grew organically out of working on more and more models. Tell me about your Facebook years. Yeah, I joined a Facebook applied research group right after I graduated. Some of the works included building very large scale video understanding models. They were deployed in different video products on Facebook and Instagram for content moderation, activity, detection, etc. And then I led an effort on building safer and responsible models. After that I spent a year at Runway, a generative AI company. That gave me an opportunity to get exposed to how to build generative image and video models. Read 160 FASCINATING interviews with Women in Science Read 160 FASCINATING interviews with Women in Science
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