Zero-shot框架用多模态模型识别足球转播事件，F1达72.0%
Zero-Shot Multimodal Event Spotting in Soccer Broadcasts
不训练就能看球赛自动挑事件，靠解说文本加分镜省72%推理成本，还超过专门训练的SoccerNet模型，做视频集锦的可以看看。
一项研究提出无需任务专项训练的zero-shot足球赛事事件识别框架，将比赛切分为重叠片段，用视觉分镜板和对齐的解说文本交由通用多模态模型联合处理。在3场人工标注、共130个事件的比赛上，最佳配置取得72.0% F1@5s，在跨数据集设置下超过SoccerNet训练的CALF和NetVLAD++。分镜表示相比逐帧输入将推理成本降低72%。框架还加入基于证据的兴奋度评分阶段，用自然语言解释为集锦筛选排序事件。
Zero-Shot Multimodal Event Spotting in Soccer Broadcasts
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.
