  A collection of approaches based on graph convolutional networks have proven
success in skeleton-based action recognition by exploring neighborhood
information and dense dependencies between intra-frame joints. However, these
approaches usually ignore the spatial-temporal global context as well as the
local relation between inter-frame and intra-frame. In this paper, we propose a
focusing and diffusion mechanism to enhance graph convolutional networks by
paying attention to the kinematic dependence of articulated human pose in a
frame and their implicit dependencies over frames. In the focusing process, we
introduce an attention module to learn a latent node over the intra-frame
joints to convey spatial contextual information. In this way, the sparse
connections between joints in a frame can be well captured, while the global
context over the entire sequence is further captured by these hidden nodes with
a bidirectional LSTM. In the diffusing process, the learned spatial-temporal
contextual information is passed back to the spatial joints, leading to a
bidirectional attentive graph convolutional network (BAGCN) that can facilitate
skeleton-based action recognition. Extensive experiments on the challenging NTU
RGB+D and Skeleton-Kinetics benchmarks demonstrate the efficacy of our
approach.
