  Human action recognition has drawn a lot of attention in the recent years due
to the research and application significance. Most existing works on action
recognition focus on learning effective spatial-temporal features from videos,
but neglect the strong causal relationship among the precondition, action and
effect. Such relationships are also crucial to the accuracy of action
recognition. In this paper, we propose to model the causal relationships based
on the precondition and effect to improve the performance of action
recognition. Specifically, a Cycle-Reasoning model is proposed to capture the
causal relationships for action recognition. To this end, we annotate
precondition and effect for a large-scale action dataset. Experimental results
show that the proposed Cycle-Reasoning model can effectively reason about the
precondition and effect and can enhance action recognition performance.
