General-purpose multimodal models can effectively spot sports events without fine-tuning by leveraging compact visual representations and aligned text, suggesting that domain-specific training may be unnecessary for event detection tasks.
This paper presents a zero-shot approach for detecting and localizing soccer events in video broadcasts using multimodal models, without requiring task-specific training. By combining visual storyboards and commentary transcripts processed by a general-purpose model, it achieves strong performance while reducing computational cost by 72% compared to frame-by-frame analysis.