Women Sport Actions Dataset for Visual Classification Using Small Scale Training Data
To address the scarcity of female sports action imagery and insufficient modeling of intra-class and inter-class variations—key bottlenecks in few-shot action recognition—this work introduces WomenSports, the first dedicated benchmark dataset for visual classification of women’s sports actions, featuring fine-grained samples across diverse scenes, poses, and attire. Methodologically, we propose a Local Context Region-based Channel Attention (LCRA) mechanism, integrated into ResNet-50 to enhance discriminative feature learning. On WomenSports, our approach achieves 89.15% Top-1 accuracy. Cross-dataset evaluation further demonstrates strong generalization capability, significantly outperforming baseline methods. This work bridges dual gaps in the field: it provides the first large-scale, gender-specific action dataset and a tailored attention architecture, thereby establishing a foundational resource for fair, robust, and inclusive sports motion analysis.