  The objective of action quality assessment is to score sports videos.
However, most existing works focus only on video dynamic information (i.e.,
motion information) but ignore the specific postures that an athlete is
performing in a video, which is important for action assessment in long videos.
In this work, we present a novel hybrid dynAmic-static Context-aware attenTION
NETwork (ACTION-NET) for action assessment in long videos. To learn more
discriminative representations for videos, we not only learn the video dynamic
information but also focus on the static postures of the detected athletes in
specific frames, which represent the action quality at certain moments, along
with the help of the proposed hybrid dynamic-static architecture. Moreover, we
leverage a context-aware attention module consisting of a temporal
instance-wise graph convolutional network unit and an attention unit for both
streams to extract more robust stream features, where the former is for
exploring the relations between instances and the latter for assigning a proper
weight to each instance. Finally, we combine the features of the two streams to
regress the final video score, supervised by ground-truth scores given by
experts. Additionally, we have collected and annotated the new Rhythmic
Gymnastics dataset, which contains videos of four different types of gymnastics
routines, for evaluation of action quality assessment in long videos. Extensive
experimental results validate the efficacy of our proposed method, which
outperforms related approaches. The codes and dataset are available at
\url{https://github.com/lingan1996/ACTION-NET}.
