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Towards Universal Soccer Video Understanding
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Introduces SoccerReplay-1988, a 1,988-match multimodal soccer dataset with automated annotations, and MatchVision, a SigLIP-initialized spatiotemporal visual encoder evaluated on event classification, commentary generation, and multi-view foul recognition.
Problem
Soccer video understanding research is fragmented across task-specific models and limited by small datasets (about 500 full matches in SoccerNet), with no unified visual encoder for diverse soccer tasks.
Approach
Pretrain a soccer-specific visual encoder by extending SigLIP with interleaved temporal and spatial attention (TimeSformer-style) on a large automatically curated match dataset, using supervised event classification and/or video-language contrastive learning, then reuse frozen features for multiple downstream heads.
Key results
- MatchVision with supervised pretraining on SoccerReplay-1988 and hybrid training reaches 80.1% top-1 event classification accuracy on SoccerNet-v2, versus 50.2% for SigLIP off-the-shelf.
- On SoccerReplay-test, MatchVision hybrid pretraining with SR achieves 33.58 BLEU-1 and 44.18 CIDEr for commentary generation, above SigLIP's 41.82 CIDEr.
- Frozen MatchVision reaches comparable multi-view foul recognition performance to jointly fine-tuned MViT (e.g., 0.58 severity top-1 with mean aggregation vs 0.38 for MViT).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MatchVision instead of SigLIP with supervised classification pretraining on SoccerNet-v2 | SoccerNet-v2 event classification · SoccerNet-v2 test | Acc.@1: 55.9 -> 82.5 | Helped |
| Use hybrid supervised-contrastive pretraining with MatchVision | SN-Caption-test-align commentary generation · SN-Caption-test-align | CIDEr: 38.24 -> 44.18 | Helped |
| Add SoccerReplay-1988 to supervised classification pretraining (ablation) | MatchVision · SoccerReplay-test event classification | Acc.@1: 62.67 -> 68.03 | Helped |
| Add SoccerReplay-1988 to visual-language contrastive pretraining (ablation) | MatchVision · SoccerReplay-test event classification | Acc.@1: 46.97 -> 57.41 | Helped |
| Add SoccerReplay-1988 to hybrid pretraining (ablation) | MatchVision · SoccerReplay-test event classification | Acc.@1: 56.86 -> 63.59 | Helped |
| Use supervised classification rather than visual-language contrastive pretraining (ablation) | MatchVision · SoccerReplay-test event classification | Acc.@1: 68.03 vs 57.41 | Helped |
| Train the visual encoder on MatchTime rather than freezing it (ablation) | MatchVision commentary generation head · SoccerReplay-test commentary generation trained on MatchTime | CIDEr: 12.90 -> 30.77 | Helped |
| Train the LLM decoder rather than freezing it (ablation) | MatchVision commentary generation head · SoccerReplay-test commentary generation trained on MatchTime | CIDEr: 12.90 -> 31.91 | Helped |
| Add SoccerReplay-1988 to commentary-generation training (ablation) | MatchVision commentary generation head · SoccerReplay-test commentary generation | CIDEr: 30.77 -> 45.85 | Helped |
| Jointly train the visual encoder and LLM decoder instead of training only the visual encoder (ablation) | MatchVision commentary generation head · SoccerReplay-test commentary generation trained on MatchTime and SoccerReplay-1988 | CIDEr: 45.85 -> 43.18 | Hurt |
Surprising findings
- Supervised classification pretraining outperforms video-language contrastive learning and hybrid pretraining on the SoccerReplay-test event classification ablation.
- A frozen MatchVision encoder matches jointly fine-tuned foul recognition baselines despite no encoder training.
Limitations
- Dataset annotations are automatically generated from textual commentaries, with about 98% accuracy on a 2% manual check.
- Data and annotations are released only for non-commercial use, with videos sourced from the Internet.
Experimental setup
Datasets: SoccerReplay-1988 SoccerNet-v2 SoccerNet-pro MatchTime MVFoul
Benchmarks: SoccerNet-v2 event classification (100 matches) SN-Caption-test-align SoccerReplay-test MVFoul
Metrics: top-1/3/5 accuracy BLEU METEOR ROUGE-L CIDEr top-1/2 foul type accuracy severity accuracy
Baselines: I3D C3D ResNet CLIP InternVideo SigLIP Baidu R(2+1)D MViT
Builds on: SigLIP TimeSformer MatchTime Perceiver
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