FEASE: Feature Selection and Enhancement Networks for Action Recognition

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.