  Sports videos pose complex challenges, including cluttered backgrounds,
camera angle changes, small action-representing objects, and imbalanced action
class distribution. Existing methods for detecting actions in sports videos
heavily rely on global features, utilizing a backbone network as a black box
that encompasses the entire spatial frame. However, these approaches tend to
overlook the nuances of the scene and struggle with detecting actions that
occupy a small portion of the frame. In particular, they face difficulties when
dealing with action classes involving small objects, such as balls or
yellow/red cards in soccer, which only occupy a fraction of the screen space.
To address these challenges, we introduce a novel approach that analyzes and
models scene entities using an adaptive attention mechanism. Particularly, our
model disentangles the scene content into the global environment feature and
local relevant scene entities feature. To efficiently extract environmental
features while considering temporal information with less computational cost,
we propose the use of a 2D backbone network with a time-shift mechanism. To
accurately capture relevant scene entities, we employ a Vision-Language model
in conjunction with the adaptive attention mechanism. Our model has
demonstrated outstanding performance, securing the 1st place in the
SoccerNet-v2 Action Spotting, FineDiving, and FineGym challenge with a
substantial performance improvement of 1.6, 2.0, and 1.3 points in avg-mAP
compared to the runner-up methods. Furthermore, our approach offers
interpretability capabilities in contrast to other deep learning models, which
are often designed as black boxes. Our code and models are released at:
https://github.com/Fsoft-AIC/unifying-global-local-feature.
