SADA: Semantic Adversarial Unsupervised Domain Adaptation for Temporal Action Localization

David Pujol-Perich, Albert Clapés, Sergio Escalera; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9219-9229

Temporal Action Localization (TAL) is a complex task that poses relevant challenges particularly when attempting to generalize on new - unseen - domains in real-world applications. These scenarios despite realistic are often neglected in the literature exposing these solutions to important performance degradation. In this work we tackle this issue by introducing for the first time an approach for Unsupervised Domain Adaptation (UDA) in sparse TAL which we refer to as Semantic Adversarial unsupervised Domain Adaptation (SADA). Our contributions are threefold: (1) we pioneer the development of a domain adaptation model that operates on realistic sparse action detection benchmarks; (2) we tackle the limitations of global-distribution alignment techniques by introducing a novel adversarial loss that is sensitive to local class distributions ensuring finer-grained adaptation; and (3) we present a novel set of benchmarks based on EpicKitchens100 and CharadesEgo that evaluate multiple domain shifts in a comprehensive manner. Our experiments indicate that SADA improves the adaptation across domains when compared to fully supervised state-of-the-art and alternative UDA methods attaining a performance boost of up to 6.14% mAP. The code is publicly available at https://github.com/davidpujol/SADA.