  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.
