While highlight detection for long-form videos is of great practical importance, most existing methods remain limited to short-form content, largely due to the absence of a suitable benchmark. To bridge this gap, we introduce SVHighlights, to the best of our knowledge, the first benchmark for highlight detection in extremely long sports videos, each exceeding one hour in duration, across multiple sports categories. SVHighlights is constructed from pairs of full-length sports videos and their corresponding official highlight videos using a dataset generation pipeline, enabling scalable label generation without conventional per-clip saliency annotation. The benchmark comprises 320 videos with an average duration of 2.00 hours and a total of 640.18 hours, substantially exceeding previous datasets. Existing methods also face fundamental challenges on long videos: models trained on short clips fail to generalize to hour-long content, and their clip-level scoring lacks the broader context needed to identify highlights. To address this and provide a strong baseline, we present TF-SELECTOR, a training-free segment-based approach that divides each video into context-aware segments by merging adjacent shots sharing the same semantic content, and predicts segment-level saliency scores using a large language model with multimodal inputs including visual captions, transcripts, and audio volume. Experiments demonstrate that TF-SELECTOR achieves superior performance across most metrics compared to Video Temporal Grounding (VTG)-tuned baselines, with improvements of +3.12 in HIT@1, +4.06 in HIT@K, and +2.95 in IoU. These results establish SVHighlights as a challenging testbed for long-form highlight detection and demonstrate that a simple segment-based strategy can effectively scale to hour-long videos.

翻译：尽管长视频的精彩片段检测具有重要的实际意义，但现有方法大多局限于短视频内容，这主要是由于缺乏合适的基准。为填补这一空白，我们提出了SVHighlights——据我们所知，这是首个针对超长体育视频精彩片段检测的基准，每段视频时长超过一小时，涵盖多个体育类别。SVHighlights通过数据集生成流水线从全时长体育视频及其对应的官方集锦视频构建而成，无需传统的逐片段显著性标注即可实现可扩展的标签生成。该基准包含320段视频，平均时长为2.00小时，总时长达到640.18小时，远超先前数据集。现有方法在长视频上面临根本性挑战：基于短片段训练的模型难以泛化至小时级内容，且其逐片段评分缺乏识别精彩片段所需的上下文信息。为解决这一问题并提供强基线，我们提出了TF-SELECTOR——一种免训练的片段级方法。该方法通过合并语义相同的相邻镜头将视频分割为上下文感知的片段，并利用多模态输入（包括视觉描述、文本转录和音频音量）的大语言模型预测片段级显著性分数。实验表明，与视频时序定位（VTG）微调的基线相比，TF-SELECTOR在大多数指标上表现更优，HIT@1提升+3.12，HIT@K提升+4.06，IoU提升+2.95。这些结果将SVHighlights确立为长视频精彩片段检测的挑战性测试平台，并证明简单的片段级策略可有效扩展至小时级视频。