ICE-GCN: An interactional channel excitation-enhanced graph convolutional network for skeleton-based action recognition

Abstract

Thanks to the development of depth sensors and pose estimation algorithms, skeleton-based action recognition has become prevalent in the computer vision community. Most of the existing works are based on spatio-temporal graph convolutional network frameworks, which learn and treat all spatial or temporal features equally, ignoring the interaction with channel dimension to explore different contributions of different spatio-temporal patterns along the channel direction and thus losing the ability to distinguish confusing actions with subtle differences. In this paper, an interactional channel excitation (ICE) module is proposed to explore discriminative spatio-temporal features of actions by adaptively recalibrating channel-wise pattern maps.