  Action quality assessment (AQA) has become an emerging topic since it can be
extensively applied in numerous scenarios. However, most existing methods and
datasets focus on single-person short-sequence scenes, hindering the
application of AQA in more complex situations. To address this issue, we
construct a new multi-person long-form video dataset for action quality
assessment named LOGO. Distinguished in scenario complexity, our dataset
contains 200 videos from 26 artistic swimming events with 8 athletes in each
sample along with an average duration of 204.2 seconds. As for richness in
annotations, LOGO includes formation labels to depict group information of
multiple athletes and detailed annotations on action procedures. Furthermore,
we propose a simple yet effective method to model relations among athletes and
reason about the potential temporal logic in long-form videos. Specifically, we
design a group-aware attention module, which can be easily plugged into
existing AQA methods, to enrich the clip-wise representations based on
contextual group information. To benchmark LOGO, we systematically conduct
investigations on the performance of several popular methods in AQA and action
segmentation. The results reveal the challenges our dataset brings. Extensive
experiments also show that our approach achieves state-of-the-art on the LOGO
dataset. The dataset and code will be released at
\url{https://github.com/shiyi-zh0408/LOGO }.
