Master’s Thesis Presentation • Artificial Intelligence • Self-supervised Video Representation Learning by Exploiting Playing Speediness Changes

Please note: This master’s thesis presentation will be given online.

Lizhe Chen, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Olga Veksler

In recent research, the self-supervised video representation learning methods have achieved improvement by exploring video’s temporal properties, such as playing speeds and temporal order. These works inspire us to exploit a new artificial supervision signal for self-supervised representation learning: the change of video playing speed.

Specifically, we formulate two novel speediness-related pretext tasks, i.e., speediness change classification and speediness change localization, that jointly supervise a shared backbone for video representation learning.

This self-supervision approach solves the tasks altogether and encourages the backbone network to learn local and long-ranged motion and context representations. It outperforms prior arts on multiple downstream tasks, such as action recognition, video retrieval, and action localization.

To join this master’s thesis presentation on Zoom, please go to https://uwaterloo.zoom.us/j/94065681032.