Towards Universal Soccer Video Understanding
As a globally celebrated sport, soccer has attracted widespread interest from
fans all over the world. This paper aims to develop a comprehensive multi-modal
framework for soccer video understanding. Specifically, we make the following
contributions in this paper: (i) we introduce SoccerReplay-1988, the largest
multi-modal soccer dataset to date, featuring videos and detailed annotations
from 1,988 complete matches, with an automated annotation pipeline; (ii) we
present the first visual-language foundation model in the soccer domain,
MatchVision, which leverages spatiotemporal information across soccer videos
and excels in various downstream tasks; (iii) we conduct extensive experiments
and ablation studies on event classification, commentary generation, and
multi-view foul recognition. MatchVision demonstrates state-of-the-art
performance on all of them, substantially outperforming existing models, which
highlights the superiority of our proposed data and model. We believe that this
work will offer a standard paradigm for sports understanding research.
6752678b4ca8baad001c64db
Jiayuan Rao
claimed_verified
2024-12-06T09:09:14.974Z
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Jiayuan Rao 饶珈源
user
Homie0609
6752678b4ca8baad001c64dc
Haoning Wu
claimed_verified
2024-12-06T09:09:16.901Z
632c7a0d1d303f5f9acf01b8
https://cdn-avatars.huggingface.co/v1/production/uploads/632c7a0d1d303f5f9acf01b8/T010IFuCp6UaOeIyWhbCk.jpeg
Haoning Wu
user
haoningwu
6752678b4ca8baad001c64dd
Hao Jiang
6752678b4ca8baad001c64de
Ya Zhang
6752678b4ca8baad001c64df
Yanfeng Wang
6752678b4ca8baad001c64e0
Weidi Xie
Towards Universal Soccer Video Understanding
As a globally celebrated sport, soccer has attracted widespread interest from
fans all over the world. This paper aims to develop a comprehensive multi-modal
framework for soccer video understanding. Specifically, we make the following
contributions in this paper: (i) we introduce SoccerReplay-1988, the largest
multi-modal soccer dataset to date, featuring videos and detailed annotations
from 1,988 complete matches, with an automated annotation pipeline; (ii) we
present the first visual-language foundation model in the soccer domain,
MatchVision, which leverages spatiotemporal information across soccer videos
and excels in various downstream tasks; (iii) we conduct extensive experiments
and ablation studies on event classification, commentary generation, and
multi-view foul recognition. MatchVision demonstrates state-of-the-art
performance on all of them, substantially outperforming existing models, which
highlights the superiority of our proposed data and model. We believe that this
work will offer a standard paradigm for sports understanding research.
A comprehensive multi-modal framework, including SoccerReplay-1988 dataset and MatchVision model, achieves state-of-the-art performance in soccer video understanding tasks.
SoccerReplay-1988 MatchVision multi-modal framework spatiotemporal information event classification commentary generation multi-view foul recognition