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

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. More specifically, a channel-wise spatial excitation (CSE) is incorporated to capture the crucial body global structure patterns to excite the spatial-sensitive channels. A channel-wise temporal excitation (CTE) is designed to learn temporal inter-frame dynamics information to excite the temporal-sensitive channels. ICE enhances different backbones as a plug-and-play module. Furthermore, we systematically investigate the strategies of graph topology and argue that complementary information is necessary for sophisticated action description. Finally, together equipped with ICE, an interactional channel excited graph convolutional network with complementary topology (ICE-GCN) is proposed and evaluated on three large-scale datasets, NTU RGB+D 60, NTU RGB+D 120, and Kinetics-Skeleton. Extensive experimental results and ablation studies demonstrate that our method outperforms other SOTAs and proves the effectiveness of individual sub-modules. The code will be published at .