Video Understanding: from Spatio-Temporal Representation Learning to Open-World Long-Term Reasoning

Abstract

Video understanding is one of the fundamental problems in computer vision with various applications, including autonomous vehicles, robot learning, and visual perception. Compared with traditional image understanding, video understanding: (i) has higher model complexity and requires learning from a much larger amount of data; (ii) requires more expensive annotations; (iii) and sometimes demands long-term video modeling and flexible open-world tasks. In this talk, I will present some of our approaches addressing these challenges, such as efficient and scalable spatiotemporal learning, cross-modal self-supervised learning of video and audio representations, and open-world instance segmentation. Finally, I will outline several potential future research directions in this area.

For more info, please follow this link.