Human activity recognition: learning with less labels and privacy preservation

Mubarak Ali Shah

doi:10.1117/12.2634965

30 May 2022 Human activity recognition: learning with less labels and privacy preservation

Mubarak Ali Shah

Proceedings Volume PC12096, Automatic Target Recognition XXXII; PC1209601 (2022) https://doi.org/10.1117/12.2634965
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In this talk, I will discuss our recent work on human activity recognition employing learning with less labels. In particular, I will present our work employing Semi-supervised learning (SSL), self-supervise learning and zero-short learning. First, I will present our Uncertainty-aware Pseudo-label Selection (UPS) method for semi-supervised learning, where the goal is to leverage a large unlabeled dataset alongside a small, labeled dataset. Next, I will present self-supervised method, TCLR: Temporal Contrastive Learning for Video Representations, which does not require labeled data. Finally, I will present Pairwise-Similarity Zero-shot Action Recognition (PS-ZAR) method, where the goal is to classify action classes which were not previously seen during training.

Conference Presentation

Citation Download Citation

Mubarak Ali Shah "Human activity recognition: learning with less labels and privacy preservation", Proc. SPIE PC12096, Automatic Target Recognition XXXII, PC1209601 (30 May 2022); https://doi.org/10.1117/12.2634965

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