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
this https URL

本研究提出 FSDA-AR，即利用非常少量的标签目标视频实现有效自适应的少样本域自适应方法，通过深度学习技术中的潜在空间语义邻接损失，以及基于图注意网络的边缘丢失技术构建推荐算法和 FSDA-AR 基准测试，并在五个数据集上进行实验和评估，结果表明与非监督域自适应相比，在需要用较少的目标样本进行标记记录的情况下，FSDA-AR 的表现不逊于非监督领域自适应，在图像、视频等领域具有机器学习、计算机视觉等方面的研究应用价值。