Balancing Known and Unknown in Open-Set Domain Adaptation via Attention - Sacilotti, André; Almeida, Jurandy (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais Estendidos da XXXVIII Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.est.2025.38315, 2025)
Open-set unsupervised domain adaptation (OS-UDA) for video action recognition is a critical yet largely underexplored problem. Real-world applications must be robust to distribution shifts between training and deployment data (domain gap) and capable of identifying actions not seen during training (open-set). We introduce OS-DTAB, a lightweight, plug-and-play Vision-Transformer block designed to enhance existing OS-UDA frameworks. OS-DTAB replaces the standard clip-aggregation mechanism with a domain-transferable attention module. This component compels the model to focus on spatio-temporal cues that are simultaneously transferable and open-set aware. Our experiments on the HMDB↔UCF benchmark show that OS-DTAB sets a new state-of-the-art, achieving a Harmonic Open-Set (HOS) score that surpasses models with more robust backbones.