Spatio-Temporal Adaptive Network With Bidirectional Temporal Difference for Action Recognition

Action Recognition is a fundamental task in computer vision field, with a wide range of applications in autonomous driving, security monitoring, etc. However, previous action recognition approaches usually suffer from the inappropriate spatio-temporal modeling or high computational consumption (e.g., 3D CNN). In this paper, we propose a novel Spatio-Temporal Adaptive Network (STANet) with bidirectional temporal difference, consisting of a Temporal Adaptive module (TA) and a Spatial Adaptive (SA) module, to sufficiently extract the crucial motion information and model the spatial pivotal appearance information from both forward and backward perspectives, respectively. Specifically, the Temporal Adaptive module uses bidirectional temporal differences to learn valuable motion trends and balance the static semantics and dynamic motion for a certain action during information fusion; while the Spatial Adaptive module uses the bidirectional temporal difference to obtain the spatio-channel attention to stress the discriminative position-relevant and semantic-relevant appearance features. Extensive experiments conducted on widely-used action recognition benchmarks UCF-101, HMDB-51, Something-Something V1, and Kinetics-400 prove the effectiveness of the proposed methods compared to other state-of-the-art approaches.