CVPR 2023 - TimeBalance: Temporally-Invariant and Temporally-Distinctive Video

In this episode we discuss TimeBalance: Temporally-Invariant and Temporally-Distinctive Video by Authors: Ishan Rajendrakumar Dave, Mamshad Nayeem Rizve, Chen Chen, Mubarak Shah Affiliation: Center for Research in Computer Vision, University of Central Florida, Orlando, USA Contact Emails: {ishandave, nayeemrizve}@knights.ucf.edu, {chen.chen, shah}@crcv.ucf.edu. The paper proposes a semi-supervised learning framework called TimeBalance for video domain tasks that utilizes self-supervised representations. Unlike existing methods that rely on hard input inductive biases, TimeBalance utilizes a temporally-invariant and a temporally-distinctive teacher to distill knowledge from unlabeled videos based on a novel temporal similarity-based reweighting scheme. The method achieves state-of-the-art performance on three action recognition benchmarks: UCF101, HMDB51, and Kinetics400. Code for TimeBalance is available on GitHub.