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 contribution is 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 experimental setup, based on EpicKitchens100, that evaluates multiple
types of domain shifts in a comprehensive manner. Our experimental results
indicate that SADA improves the adaptation across domains when compared to
fully supervised state-of-the-art and alternative UDA methods, attaining a
relative performance boost of up to 14%.

本文介绍了一种针对稀疏时序行为定位的无监督领域自适应方法，称为语义对抗无监督领域自适应 (SADA)。通过引入一种对局部类别分布敏感的新型对抗损失函数，SADA 在多个领域之间的自适应中显著提升性能。相比于完全监督的最先进方法和其他 UDA 方法，SADA 在多种领域转移情况下实现了相对性能提升高达 14%。