Hierarchical Semantics Interaction for Compressed Video Action Recognition

Directly recognizing action based on compressed video shows significant advantages, such as low storage demands, efficient decoding, and fast inference speeds. Existing methods on compressed video achieve promising performance by separately modeling spatial and motion cues and directly fusing recognition results of I-frames and P-frames. However, these approaches overlook the following inherent attributes of compressed videos: 1) Temporal misalignment between I-frames and P-frames impairs the accuracy of action recognition. 2) Spatiotemporal sparsity of compressed video frames severely hinders the semantic modeling of complex actions. 3) Semantic discrepancy between I-frames and P-frames, which capture appearance and motion information respectively, leads to suboptimal performance when they are fused directly. To address these challenges, we propose a Hierarchical Semantics Interaction Network (HSINet) that ensures refined semantic modeling of compressed video through alignment, interaction, and calibration within a hierarchical fusion framework. Specifically, to resolve temporal misalignment, we propose an efficient cross-modal temporal alignment module that fully combines the spatiotemporal information of P-frames and the spatial information of I-frames, and includes both pre-alignment and fine alignment stages. To mitigate semantic degradation caused by sparsity, we propose a cross-modal semantics interaction module to provide the multi-scale semantics interaction between spatial and temporal representation learning and enhance representations’ spatiotemporal awareness. To calibrate the semantic imbalance between I-frames and P-frames, we propose a modal imbalance calibration module that optimizing directional differences via cosine similarity in hyperspherical space. Experiments on HMDB-51, UCF-101, and Kinetics-400 benchmarks, demonstrate the effectiveness of hierarchical semantic interaction for compressed video action recognition.