Multiheads Attention Graph Convolutional Networks for SkeletonBased Action Recognition Compared with videobased action recognition, skeletonbased methods have more compact and accurate representation. In the recent development, human skeleton is modeled as graph and graphbased deep learning method is applied for recognition. Generally, in actions, few joints are pivotal other than the whole body, like waving hands mostly related with hand and arm joints. Hence, we propose the datadriven multihead attention model for graph convolutional networks. The attention model identifies key joints of every action by introducing two regularization terms, i.e., spatial diversity and local continuity. Further, we introduce the jointwise second order motion information as the additional feature on the graph node, which represents the motion variation explicitly. We have tested our methods on the largest popular dataset, NTURGBD, and we reach stateoftheart performance.

Improving Action Recognition with the GraphNeuralNetworkbased Interaction Reasoning Recent human action recognition methods mainly model a twostream or 3D convolution deep learning network, with which humans spatialtemporal features can be exploited and utilized effectively. However, due to the ignoring of interaction exploiting, most of these methods cannot get good enough performance. In this paper, we propose a novel action recognition framework with Graph Convolutional Network (GCN) based Interaction Reasoning: Objects and discriminative scene patches are detected using an object detector and class active mapping (CAM), respectively; and then a GCN is introduced to model the interaction among the detected objects and scene patches. Evaluation of two widely used video action benchmarks shows that the proposed work can achieve comparable performance: the accuracy up to 43.6% at EPIC Kitchen, and 47.0% at VLOG benchmark without using optical flow, respectively.