Zero-Shot Multimodal Event Spotting in Soccer Broadcasts

Sadia Kamal; Mahmudul Hasan; Tony Braskich

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arXiv · 2610.37583

Zero-Shot Multimodal Event Spotting in Soccer Broadcasts

Sadia Kamal·Mahmudul Hasan·Tony Braskich

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Abstract

Automatic soccer event spotting supports highlight generation, video indexing, and sports analytics, but existing methods depend on large annotated datasets and soccer-specific training. We present a zero-shot framework that detects, classifies, and temporally localizes events in full-length soccer broadcasts without task-specific parameter training. Each match is segmented into overlapping clips represented by a compact visual storyboard and the aligned commentary transcript, which a general-purpose multimodal model processes jointly. On three manually annotated matches containing 130 events, the best configuration achieves 72.0% F1@5s and outperforms SoccerNet-trained CALF and NetVLAD++ in a cross-dataset setting, while the storyboard representation reduces inference cost by 72% compared with individual frames. We further analyze the effects of model family, input representation, and prompt optimization, and introduce an evidence-grounded excitement-scoring stage that ranks detected events with natural-language explanations for highlight selection.

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Sadia Kamal, Mahmudul Hasan, Tony Braskich. 2026-10-27. Zero-Shot Multimodal Event Spotting in Soccer Broadcasts. https://arxiv.org/abs/2610.37583

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