Domain adaptation is essential for activity recognition to ensure accurate and robust performance across diverse environments, sensor types, and data sources. Unsupervised domain adaptation methods have been extensively studied, yet, they require large-scale unlabeled data from the target domain. In this work, we focus on Few-Shot Domain Adaptation for Activity Recognition (FSDA-AR), which leverages a very small amount of labeled target videos to achieve effective adaptation. This approach is appealing for applications because it only needs a few or even one labeled example per class in the target domain, ideal for recognizing rare but critical activities. However, the existing FSDA-AR works mostly focus on the domain adaptation on sports videos, where the domain diversity is limited. We propose a new FSDA-AR benchmark using five established datasets considering the adaptation on more diverse and challenging domains. Our results demonstrate that FSDA-AR performs comparably to unsupervised domain adaptation with significantly fewer labeled target domain samples. We further propose a novel approach, RelaMiX, to better leverage the few labeled target domain samples as knowledge guidance. RelaMiX encompasses a temporal relational attention network with relation dropout, alongside a cross-domain information alignment mechanism. Furthermore, it integrates a mechanism for mixing features within a latent space by using the few-shot target domain samples. The proposed RelaMiX solution achieves state-of-the-art performance on all datasets within the FSDA-AR benchmark. To encourage future research of few-shot domain adaptation for activity recognition, our code will be publicly available at https://github.com/KPeng9510/RelaMiX.

翻译：领域适应对于行为识别至关重要，可确保跨不同环境、传感器类型和数据源的准确性与鲁棒性。无监督领域适应方法已被广泛研究，但它们需要大规模目标域无标签数据。本研究聚焦于行为识别的少样本领域适应（FSDA-AR），该方法利用极少量带标签的目标视频实现有效适应。该方案具有应用吸引力，因其仅需目标域每类少量甚至单个标签样本，特别适用于识别罕见但关键的行为。然而，现有FSDA-AR研究主要聚焦于领域多样性有限的体育视频领域。我们利用五个已建立数据集构建了新的FSDA-AR基准，针对更复杂多样的领域适应场景。实验结果表明，FSDA-AR在显著减少目标域标签样本数量的情况下，性能可与无监督领域适应相媲美。我们进一步提出创新方法RelaMiX，以更好地利用少量带标签目标域样本作为知识引导。RelaMiX包含带关系丢弃机制的时间关系注意力网络，以及跨域信息对齐机制。此外，该方法通过使用少样本目标域样本在潜在空间内融合特征。所提出的RelaMiX解决方案在FSDA-AR基准的所有数据集上均实现了最先进性能。为促进行为识别领域少样本域适应研究的未来发展，我们的代码将开源在https://github.com/KPeng9510/RelaMiX。