Faster-slow network fused with enhanced fine-grained features for action recognition

Two-stream methods, which separate human actions and backgrounds into temporal and spatial streams visually, have shown promising results in action recognition datasets. However, prior researches emphasize motion modeling but overlook the robust correlation between motion features and spatial information, causing restriction of the model’s ability to recognize behaviors entailing occlusions or rapid changes. Therefore, we introduce Faster-slow, an improved framework for frame-level motion features. It introduces a Behavioural Feature Enhancement (BFE) module based on a novel two-stream network with different temporal resolutions. BFE consists of two components: MM, which incorporates motion-aware attention to capture dependencies between adjacent frames; STC, which enhances spatio-temporal and channel information to generate optimized features. Overall, BFE facilitates the extraction of finer-grained motion information, while ensuring a stable fusion of information across both streams. We evaluate the Faster-slow on the Atomic Visual Actions dataset, and the Faster-AVA dataset constructed in this paper, yielding promising experimental results.