SVHighlights: Towards Extremely Long Sport Video Highlight Detection
Year: 2026
Type: conference-paper
Abstract: 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 and cost-effective label generation without conventional per-clip saliency annotation. The benchmark comprises 320 videos spanning a wide range of sports, with an average duration of 2.00 hours and a total of 640.18 hours, substantially exceeding previous highlight detection datasets. Beyond the lack of benchmarks, existing methods also face fundamental challenges on long videos: models trained on short clips of only a few minutes fail to generalize to hour-long content, and their clip-level scoring lacks the broader context needed to identify highlights in long-form videos. To address these challenges and provide a strong baseline for SVHighlights, 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 (LLM) with multimodal inputs including visual captions, transcripts, and audio volume. Extensive experiments demonstrate that TF-SELECTOR achieves superior performance across most evaluation metrics compared to Video Temporal Grounding (VTG)-tuned baselines, with improvements of +2.50 in HIT@1, +4.04 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. The dataset and code are available at https://leedongkyu2019.github.io/SVHighlights/.
Institution: Ulsan National Institute of Science and Technology
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7164022282
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