FEASE: Feature Selection and Enhancement Networks for Action Recognition
Lu Zhou, Yuanyao Lu, Haiyang Jiang · Neural Processing Letters · 2024
Abstract Reinforcement of motor features is necessary in action recognition tasks. In this work, we propose an efficient feature reinforcement model, termed as Feature Selection and Enhancement Networks (FEASE-Net). The core of our FEASE-Net is the use of the FEASE module to adaptively capture input features at multi-scales and reinforce them globally. FEASE module is composed of two sub-module, Feature Selection (FS) and Feature Enhancement (FE). The FS focuses on adaptive attention and selection of input features through a multi-scale structure with an attention mechanism, and FE employs channel attention to enhance the global useful feature information. To assess the effectiveness of FEASE-Net, we undertake a series of extensive experiments on two benchmark datasets, namely Kinetics 400 and Something-Something V2. Our proposed FEASE-Net can achieve a competitive performance compared with previous state-of-the-art methods that use similar backbones.