Shot Action Recognition

Few-shot action recognition (FSAR) aims to classify actions in videos using only a limited number of labeled examples per action class, addressing the high cost of manual video annotation. Current research heavily focuses on improving feature extraction and matching through techniques like multi-modal learning (combining visual and textual information), task-specific adapters for pre-trained models, and advanced temporal alignment methods (e.g., Dynamic Time Warping variations). These advancements are crucial for enabling robust action recognition in data-scarce scenarios, with implications for applications ranging from video surveillance to human-computer interaction. The field is actively exploring both supervised and unsupervised approaches, including cross-domain generalization to further enhance model efficiency and applicability.