Few-shot action recognition seeks to classify new action categories using only a few labeled video samples as reference. Due to the lack of sufficient training samples and the complex structure of video data, it is difficult to extract global features that can be directly used for classification. Therefore, most previous works adopt image encoders to extract the features of each frame individually, and then perform temporal fusion and alignment for the query and support features. However, they neglect the importance of sufficient spatiotemporal modeling and relationships with other categories in the few-shot task when extracting features, rendering them less effective at distinguishing between the given action classes, especially those that require the perception of local motion. In this paper, we present Hierarchical Task-aware Temporal Modeling and Matching (HTTMM) to better perceive critical motion patterns and extract …
本文针对少样本动作识别中训练样本稀缺且视频结构复杂的问题，提出分层任务感知时间建模与匹配方法（HTTMM）。该方法通过分层结构充分建模时空特征，并利用任务感知机制增强对关键运动模式的感知，从而提升查询样本与支持样本之间的匹配效果。在多个基准数据集上的实验验证了其有效性，尤其适用于需要局部运动感知的细粒度动作分类任务。