Task-Aware Attentional Dynamic Alignment for Few-Shot Compressed Video Classification

We present a novel Task-aware Attentional Dynamic Alignment (TADA) framework for visual-based few-shot video classification (FSVC) that addresses two key challenges in this field: efficiency and nuanced spatio-temporal reasoning. Existing methods are often hindered by computationally expensive video decoding processes and neglect the temporal order of videos. In contrast, our method harnesses compressed domain data to extract rich spatio-temporal cues at a fraction of the cost of traditional video processing methods. Specifically, we propose an embedding module to extract informative features from compressed domain data while minimizing computational overheads. Furthermore, to exploit the temporal order of frames, we develop a prototypical ADA module to align and classify videos with an explicit temporal order constraint. Our framework also incorporates a contextual mixer to enrich video embeddings with task-specific context. Extensive experiments on multiple datasets demonstrate that TADA achieves state-of-the-art performance and outperforms existing methods in accuracy and efficiency.