  In this paper, we introduce ASTRA, a Transformer-based model designed for the
task of Action Spotting in soccer matches. ASTRA addresses several challenges
inherent in the task and dataset, including the requirement for precise action
localization, the presence of a long-tail data distribution, non-visibility in
certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a
Transformer encoder-decoder architecture to achieve the desired output temporal
resolution and to produce precise predictions, (b) a balanced mixup strategy to
handle the long-tail distribution of the data, (c) an uncertainty-aware
displacement head to capture the label variability, and (d) input audio signal
to enhance detection of non-visible actions. Results demonstrate the
effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set.
Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd
position with an Average-mAP of 70.21 on the challenge set.
