# SVHighlights: Towards Extremely Long Sport Video Highlight Detection

arXiv: https://arxiv.org/abs/2606.06926 · HTML: https://mlsanity.com/paper/2606.06926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2203.12745/) (2022)
- [TRACE: Temporal Grounding Video LLM via Causal Event Modeling](https://mlsanity.com/paper/2410.05643/) (2024)
- [VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding](https://mlsanity.com/paper/2405.13382/) (2024)
- [TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2401.02309/) (2024)
- [Video2GIF: Automatic Generation of Animated GIFs from Video](https://mlsanity.com/paper/1605.04850/) (2016)
- [MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer](https://mlsanity.com/paper/2305.00355/) (2023)
- [Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding](https://mlsanity.com/paper/2311.08835/) (2023)
- [Adaptive Video Highlight Detection by Learning from User History](https://mlsanity.com/paper/2007.09598/) (2020)
- [PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation](https://mlsanity.com/paper/1804.06604/) (2018)
- [Shorts vs. Regular Videos on YouTube: A Comparative Analysis of User Engagement and Content Creation Trends](https://mlsanity.com/paper/2403.00454/) (2024)
- [Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence](https://mlsanity.com/paper/2407.13933/) (2024)
- [From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding](https://mlsanity.com/paper/2507.02790/) (2025)

## Abstract (arXiv)

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 +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.