Domain adaptation is essential for activity recognition, as common spatiotemporal architectures risk overfitting due to increased parameters arising from the temporal dimension. Unsupervised domain adaptation methods have been extensively studied, yet, they require large-scale unlabeled data from the target domain. In this work, we address few-shot domain adaptation for video-based activity recognition (FSDA-AR), which leverages a very small amount of labeled target videos to achieve effective adaptation. This setting is attractive and promising for applications, as it requires recording and labeling only a few, or even a single example per class in the target domain, which often includes activities that are rare yet crucial to recognize. We construct FSDA-AR benchmarks using five established datasets: UCF101, HMDB51, EPIC-KITCHEN, Sims4Action, and Toyota Smart Home. Our results demonstrate that FSDA-AR performs comparably to unsupervised domain adaptation with significantly fewer (yet labeled) target examples. We further propose a novel approach, FeatFSDA, to better leverage the few labeled target domain samples as knowledge guidance. FeatFSDA incorporates a latent space semantic adjacency loss, a domain prototypical similarity loss, and a graph-attentive-network-based edge dropout technique. Our approach achieves state-of-the-art performance on all datasets within our FSDA-AR benchmark. To encourage future research of few-shot domain adaptation for video-based activity recognition, we will release our benchmarks and code at https://github.com/KPeng9510/FeatFSDA.

翻译：摘要：领域自适应对于行为识别至关重要，因为常见的时空架构因时间维度引入的参数增加而易出现过拟合。无监督领域自适应方法已被广泛研究，但其需要大量来自目标领域的无标签数据。本文针对视频行为识别中的少样本领域自适应问题（FSDA-AR），利用极少量带标签的目标视频实现有效自适应。该设定具有应用吸引力与前景，因为只需对目标领域每类活动标注少量甚至单个样本即可完成，这些活动往往罕见却至关重要。我们基于UCF101、HMDB51、EPIC-KITCHEN、Sims4Action和Toyota Smart Home五个公开数据集构建FSDA-AR基准。实验表明，FSDA-AR在显著减少目标样本数量（但仍带标签）的情况下，性能与无监督领域自适应相当。我们进一步提出新方法FeatFSDA，以更有效地利用少量带标签目标域样本作为知识引导。FeatFSDA融合了潜在空间语义邻接损失、领域原型相似性损失以及基于图注意力网络的边丢弃技术。该方法在所有FSDA-AR基准数据集上均达到最优性能。为促进视频行为识别少样本领域自适应的未来研究，我们将公开基准与代码，链接为https://github.com/KPeng9510/FeatFSDA。