  Action recognition has long been a fundamental and intriguing problem in
artificial intelligence. The task is challenging due to the high dimensionality
nature of an action, as well as the subtle motion details to be considered.
Current state-of-the-art approaches typically learn from articulated motion
sequences in the straightforward 3D Euclidean space. However, the vanilla
Euclidean space is not efficient for modeling important motion characteristics
such as the joint-wise angular acceleration, which reveals the driving force
behind the motion. Moreover, current methods typically attend to each channel
equally and lack theoretical constrains on extracting task-relevant features
from the input.
  In this paper, we seek to tackle these challenges from three aspects: (1) We
propose to incorporate an acceleration representation, explicitly modeling the
higher-order variations in motion. (2) We introduce a novel Stream-GCN network
equipped with multi-stream components and channel attention, where different
representations (i.e., streams) supplement each other towards a more precise
action recognition while attention capitalizes on those important channels. (3)
We explore feature-level supervision for maximizing the extraction of
task-relevant information and formulate this into a mutual information loss.
Empirically, our approach sets the new state-of-the-art performance on three
benchmark datasets, NTU RGB+D, NTU RGB+D 120, and NW-UCLA. Our code is
anonymously released at https://github.com/ActionR-Group/Stream-GCN, hoping to
inspire the community.
