Abstract
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.
Originalsprog | Engelsk |
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Titel | MMSports 2023 - Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports, Co-located with : MM 2023 |
Antal sider | 10 |
Forlag | Association for Computing Machinery |
Publikationsdato | 29 okt. 2023 |
Sider | 93-102 |
ISBN (Elektronisk) | 9798400702693 |
DOI | |
Status | Udgivet - 29 okt. 2023 |
Begivenhed | 6th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2023, co-located with ACM Multimedia 2023 - Ottawa, Canada Varighed: 29 okt. 2023 → 29 okt. 2023 |
Konference
Konference | 6th ACM International Workshop on Multimedia Content Analysis in Sports, MMSports 2023, co-located with ACM Multimedia 2023 |
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Land/Område | Canada |
By | Ottawa |
Periode | 29/10/2023 → 29/10/2023 |
Sponsor | ACM SIGMM |
Navn | MMSports 2023 - Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports, Co-located with: MM 2023 |
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Bibliografisk note
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