  Pose-based action recognition has drawn considerable attention recently.
Existing methods exploit the joint positions to extract the body-part features
from the activation map of the convolutional networks to assist human action
recognition. However, these features are simply concatenated or max-pooled in
previous works. The structured correlations among the body parts, which are
essential for understanding complex human actions, are not fully exploited. To
address the problem, we propose a pose-based graph convolutional network
(PGCN), which encodes the body-part features into a human-based spatiotemporal
graph, and explicitly models their correlations with a novel light-weight
adaptive graph convolutional module to produce a highly discriminative
representation for human action recognition. Besides, we discover that the
backbone network tends to identify patterns from the most discriminative areas
of the input regardless of the others. Thus the features pooled by the joint
positions from other areas are less informative, which consequently hampers the
performance of the followed aggregation process for recognizing actions. To
alleviate this issue, we introduce a simple intermediate dense supervision
mechanism for the backbone network, which adequately addresses the problem with
no extra computation cost during inference. We evaluate the proposed approach
on three popular benchmarks for pose-based action recognition tasks, i.e.,
Sub-JHMDB, PennAction and NTU-RGBD, where our approach significantly
outperforms state-of-the-arts without the bells and whistles.
