SVHighlights: Towards Extremely Long Sport Video Highlight Detection

2026-06-05 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

SVHighlights introduces the first benchmark for highlight detection in extremely long sports videos, each exceeding one hour, across multiple sports categories. This benchmark addresses the limitation of existing methods, which are typically restricted to short-form content due to a lack of suitable datasets. SVHighlights comprises 320 videos, averaging 2.00 hours each, totaling 640.18 hours, generated from full-length sports videos and their official highlights, enabling scalable label generation. To overcome challenges faced by current models on long videos, the paper presents TF-SELECTOR, a training-free segment-based approach. TF-SELECTOR divides videos into context-aware segments and uses a large language model with multimodal inputs, including visual captions, transcripts, and audio volume, to predict segment-level saliency. Experiments show TF-SELECTOR outperforms Video Temporal Grounding (VTG)-tuned baselines, with improvements of +3.12 in HIT@1, +4.06 in HIT@K, and +2.95 in IoU.

Key takeaway

For Computer Vision Engineers developing highlight detection systems for long-form sports content, SVHighlights provides a critical benchmark to validate models on hour-long videos. You should consider adopting a segment-based processing strategy, like TF-SELECTOR, which effectively scales by leveraging multimodal large language models for context-aware saliency scoring. This approach offers significant performance gains over traditional clip-level methods, improving HIT@1 by +3.12.

Key insights

The SVHighlights benchmark and TF-SELECTOR method enable effective highlight detection in extremely long sports videos.

Principles

• Long-form video benchmarks are crucial for progress.
• Segment-based processing scales to hour-long videos.
• Multimodal LLMs enhance context-aware saliency prediction.

Method

TF-SELECTOR divides videos into context-aware segments by merging semantically similar adjacent shots. It then predicts segment-level saliency using a large language model with multimodal inputs.

In practice

• Use SVHighlights for long-form video research.
• Apply segment merging for video processing.
• Integrate multimodal LLMs for context-rich analysis.

Topics

• Sport Video Analysis
• Highlight Detection
• Long-form Video
• SVHighlights Benchmark
• TF-SELECTOR
• Multimodal LLMs

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.