Abstract

Videos are an appealing source of data to train computer vision models. There exist almost infinite supplies of videos online, but exhaustive manual annotation is infeasible. In this talk, I will briefly introduce a few methods to learn strong video representations efficiently via self-supervised learning -- a method that learns from the data rather than human annotations.

Biography

Tengda Han is a post-doctoral research fellow at the Visual Geometry Group at the University of Oxford. He obtained his PhD degree from the same group in 2022 supervised by Andrew Zisserman. His research is focusing on learning video representations with minimal supervision.

• Admission

Zoom Meeting ID: 871 5637 1274

Password: 453584