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arxiv: 2606.06926 · v2 · pith:B3ES5CXXnew · submitted 2026-06-05 · 💻 cs.CV · cs.MM

SVHighlights: Towards Extremely Long Sport Video Highlight Detection

Donggyu Lee , Youngbin Ki , Jeonghun Kang , Taehwan Kim This is my paper

Pith reviewed 2026-06-27 22:28 UTC · model grok-4.3

classification 💻 cs.CV cs.MM
keywords video highlight detectionlong-form videosports video analysisbenchmark datasettraining-free methodmultimodal large language modeltemporal grounding
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The pith

SVHighlights supplies the first benchmark for detecting highlights in sports videos longer than one hour by pairing full games with official recaps.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing highlight detectors are trained on short clips and cannot handle hour-long sports footage because they lack both suitable data and mechanisms for long-range context. The paper constructs SVHighlights from 320 full-length videos and their official highlight versions, yielding scalable saliency labels without per-clip human annotation. It then introduces TF-SELECTOR, which merges adjacent shots into context-aware segments and scores each segment with a multimodal large language model that receives visual captions, transcripts, and audio volume. Experiments show this segment-based, training-free method beats Video Temporal Grounding baselines by 2.50 points in HIT@1, 4.04 in HIT@K, and 2.95 in IoU. The result is a challenging testbed that demonstrates segment-level reasoning can scale to videos averaging two hours.

Core claim

SVHighlights is the first benchmark for highlight detection in extremely long sports videos exceeding one hour, built by matching full-length videos to official highlight videos for scalable label generation, and TF-SELECTOR, a training-free approach that divides videos into semantic segments and predicts saliency via multimodal LLM inputs, outperforms VTG-tuned baselines on this benchmark.

What carries the argument

TF-SELECTOR, which merges adjacent shots sharing semantic content into segments and feeds visual captions, transcripts, and audio volume to a large language model for segment-level saliency scores.

If this is right

  • Label generation for long videos becomes scalable without exhaustive per-clip annotation.
  • Models can process hour-long content by operating on merged segments rather than fixed short clips.
  • Multimodal inputs including audio volume improve saliency prediction over vision-only or text-only baselines.
  • A single training-free method can serve as a strong baseline across multiple sports categories.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pairing strategy could label highlights in other long-form domains such as lectures or surveillance footage.
  • Segment merging might reduce compute cost for downstream tasks like summarization or search in long videos.
  • If official highlights contain editorial bias, the benchmark may systematically under-represent certain event types.

Load-bearing premise

Matching full-length sports videos to their official highlight videos produces accurate, unbiased ground-truth labels for saliency.

What would settle it

Human annotators rating saliency on a random sample of clips produce labels that diverge substantially from the official-highlight-derived labels on the same clips.

Figures

Figures reproduced from arXiv: 2606.06926 by Donggyu Lee, Jeonghun Kang, Taehwan Kim, Youngbin Ki.

Figure 1
Figure 1. Figure 1: Average video duration (in minutes) for each video [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Video length distribution across categories. (Top) [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the highlight alignment pipeline. (a) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example of highlight alignment and filtering results on a baseball video. Each column shows a ground-truth highlight [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Overview of the TF-SELECTOR framework. Stage 1 (Context-aware segmentation): Shots are detected by a shot [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt used for segment-level score prediction. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: An example of the manual filtering process. We visualized the alignment results as grid images to manually inspect [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
read the original 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims to introduce SVHighlights as the first benchmark for highlight detection in extremely long sports videos (>1 hour each), built from 320 videos (avg. 2h, total 640h) by aligning full-length content with official highlight reels to generate labels scalably without per-clip annotation. It further proposes TF-SELECTOR, a training-free segment-based baseline that merges semantically similar shots and scores segments via LLM using multimodal inputs (captions, transcripts, audio volume). Experiments report TF-SELECTOR outperforming VTG-tuned baselines by +2.50 HIT@1, +4.04 HIT@K, and +2.95 IoU.

Significance. If the generated labels prove reliable, the work supplies a much-needed large-scale testbed for long-form video saliency that prior short-clip datasets cannot address, and the training-free LLM-based approach demonstrates a practical path to scaling without retraining on hour-long content. The dataset scale and avoidance of per-clip annotation are concrete strengths.

major comments (3)
  1. [Dataset Generation Pipeline] Dataset construction section: The central claim that temporal alignment between full-length videos and official highlights yields accurate, unbiased saliency ground truth is unsupported by any reported human-agreement study, inter-annotator consistency check, or comparison against conventional per-clip annotations. This directly undermines all quantitative results, including the reported +2.50 HIT@1, +4.04 HIT@K, and +2.95 IoU gains of TF-SELECTOR.
  2. [Experiments] Experiments section: Performance improvements are stated as point estimates without error bars, statistical significance tests, details of the train/test split protocol, or ablation studies on segment merging and LLM prompting choices. This makes it impossible to assess whether the superiority claim is robust.
  3. [Method] TF-SELECTOR description: The method for merging adjacent shots and constructing the multimodal LLM prompt is presented only conceptually, with no pseudocode, exact merging criteria, or input formatting details. Reproducibility of the core baseline therefore cannot be verified from the text.
minor comments (2)
  1. The abstract states 'to the best of our knowledge' the first benchmark; a brief comparison table against prior long-video datasets would strengthen the novelty claim.
  2. Notation for HIT@K and IoU should be defined on first use in the main text rather than assumed from the abstract.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments. We address each of the major comments below, indicating the revisions we plan to make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Dataset Generation Pipeline] Dataset construction section: The central claim that temporal alignment between full-length videos and official highlights yields accurate, unbiased saliency ground truth is unsupported by any reported human-agreement study, inter-annotator consistency check, or comparison against conventional per-clip annotations. This directly undermines all quantitative results, including the reported +2.50 HIT@1, +4.04 HIT@K, and +2.95 IoU gains of TF-SELECTOR.

    Authors: We acknowledge the importance of validating the generated labels. The SVHighlights dataset is constructed by aligning full-length videos with official highlight reels, which are produced by domain experts and represent authoritative selections of highlights. While we did not include a human study in the original submission, we agree this would enhance credibility. In the revision, we will add a human evaluation study on a subset of videos to report inter-annotator agreement and consistency with the generated labels. This addresses the concern about the reliability of the ground truth. revision: yes

  2. Referee: [Experiments] Experiments section: Performance improvements are stated as point estimates without error bars, statistical significance tests, details of the train/test split protocol, or ablation studies on segment merging and LLM prompting choices. This makes it impossible to assess whether the superiority claim is robust.

    Authors: We agree that providing error bars, statistical tests, split details, and ablations would improve the robustness assessment. In the revised manuscript, we will include standard deviations from multiple runs where applicable, p-values for significance, explicit description of the train/test split protocol, and ablation studies on the segment merging criteria and LLM prompt variations. revision: yes

  3. Referee: [Method] TF-SELECTOR description: The method for merging adjacent shots and constructing the multimodal LLM prompt is presented only conceptually, with no pseudocode, exact merging criteria, or input formatting details. Reproducibility of the core baseline therefore cannot be verified from the text.

    Authors: We will enhance the method section with pseudocode for the shot merging algorithm, precise criteria (such as semantic similarity thresholds using embeddings), and detailed examples of the multimodal prompt formatting including how captions, transcripts, and audio volume are integrated. This will ensure full reproducibility. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark and training-free method rest on external comparisons

full rationale

The paper introduces SVHighlights via a dataset pipeline matching full videos to official highlights and evaluates TF-SELECTOR (a segment-merging + LLM scoring baseline) against VTG-tuned methods using standard metrics. No equations, fitted parameters, or derivations appear that reduce any reported gain (+2.50 HIT@1 etc.) to the inputs by construction. No self-citations are load-bearing for uniqueness theorems or ansatzes; the central claims are falsifiable via external baselines and do not rename known results or smuggle assumptions through prior author work. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the validity of the official-highlight alignment as ground truth and the semantic shot-merging step for context; no free parameters, invented entities, or additional axioms are apparent from the abstract.

axioms (1)
  • domain assumption Official highlight videos serve as reliable proxies for human-perceived saliency without per-clip annotation
    Invoked to enable scalable label generation for the benchmark.

pith-pipeline@v0.9.1-grok · 5837 in / 1289 out tokens · 24942 ms · 2026-06-27T22:28:41.733501+00:00 · methodology

discussion (0)

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Reference graph

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