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SGWIB:Sliced Gromov-Wasserstein Information Bottleneck for Video Highlight Detection

Hanjuan Huang, Yung-Chieh Yeh, Hsing-Kuo Pao

arXiv:2609.13966v1cs.CVcs.AI

TL;DR

Video highlight detection needs segment representations that preserve temporal relationships, but conventional information bottlenecks do not explicitly preserve that structure. SGWIB combines a sliced structural regularizer with contextual disentanglement, and achieves the best reported ranking and retrieval metrics among compared single-modal methods on both datasets.

  • Problem

    Video highlight detection needs both discriminative segment representations and preserved temporal relations, while conventional information bottleneck methods do not explicitly preserve relational geometry and IB has not been systematically investigated for VHD.

    Source: §I · p. 1

  • Method

    SGWIB uses SGMG to regularize structural compression and combines it with HAR-CPL-guided disentanglement of task-oriented and context-oriented information.

    Source: §I · pp. 1–2

  • Results

    SGWIB achieves the best Kendall’s τ, Spearman’s ρ, mAP@50, and mAP@30 among compared single-modal methods on both MrHiSum and MoSu.

    Source: §I · p. 2

  • Takeaways & Limitations

    Experiments support combining structure-aware bottleneck regularization and contextual disentanglement for segment-level highlight prediction.

    Source: §V · p. 11

Abstract

from arXiv · show

Video highlight detection aims to identify temporally important segments that capture the most informative or engaging events in a video. Reliable prediction therefore requires not only discriminative segment representations but also preservation of the temporal relationships among neighboring and distant segments. The information bottleneck principle has proven effective for learning compact and task-relevant representations, yet it has not been explored for video highlight detection, and applying conventional formulations directly would overlook inter-segment relational structure and distort highlight relevant temporal organization during compression. We therefore introduce the Sliced Gromov-Monge Gap (SGMG), a structure aware regularizer that measures the excess relational distortion induced by a prescribed source-to-bottleneck mapping relative to an optimal sliced structural correspondence. Building on SGMG, we develop SGWIB, an information-bottleneck framework for single-modal video highlight detection that learns compact bottleneck representations while preserving inter-segment temporal structure. We further introduce Home-Away-Related Contextual Pseudo-Labels and a contextual disentanglement module that reduce sports-specific contextual bias by separating highlight oriented information from contextual patterns. Experiments on MrHiSum and MoSu show that SGWIB attains the best Kendall's tau, Spearman's rho, mAP@50, and mAP@30 among the compared single-modal methods on both datasets. On MrHiSum, the visual model improves the strongest previous results by 0.031, 0.031, 0.87, and 0.75 on these four metrics, respectively. These results show that structure-aware information-bottleneck regularization combined with contextual disentanglement improves segment-level highlight prediction.

I. INTRODUCTION

SGWIB addresses the VHD need to preserve temporal relationships while learning task-relevant representations, and combines structural compression with contextual disentanglement. Experiments report improved ranking and retrieval across MrHiSum and MoSu.

I. INTRODUCTION

  • VHD requires discriminative segment representations and preservation of temporal relations, while conventional IB has not been systematically investigated for VHD and does not explicitly preserve relational geometry.Full Gromov–Wasserstein optimization is described as prohibitively expensive for long, high-dimensional video sequences.Source: §I · p. 1
  • SGMG measures excess relational distortion from a prescribed source-to-bottleneck mapping against an optimal sliced structural correspondence, making structure-aware compression tractable through sliced projections.Its expected value controls an upper bound on empirical sliced kernelized dependence, not an exact bound on Shannon mutual information.Source: §I · pp. 1–2
  • HAR-CPLs provide weak video-level contextual supervision, and HAR-CDM separates task-oriented from context-oriented representations to reduce sports-specific contextual bias.HAR-CDM orthogonality regularization applies to valid videos, while pseudo-environment supervision is limited to sports videos; SGWIB regularizes task-oriented structure for all valid videos.Source: §I · p. 2
  • SGWIB attains the best Kendall’s τ, Spearman’s ρ, mAP@50, and mAP@30 among compared methods on both MrHiSum and MoSu.Ablations report complementary effects from HAR-CDM and SGWIB; comparisons with KL-based regularization report stronger structural preservation at modest additional training cost.Source: §I · p. 2

II. RELATED WORK

  • Prior VHD work emphasizes representation enhancement, temporal modeling, semantic alignment, generative summarization, and multimodal interaction, leaving compression effects on inter-segment structure less explored.This work positions structure-aware IB as complementary to those directions.Source: §II · p. 2

A. Information Bottleneck and Structural Dependence

  • Conventional variational IB uses KL divergence to a predefined prior, whereas SGMG is a mapping-aware structural compression surrogate rather than an exact or universal Shannon mutual-information bound.GWIB relates empirical kernelized dependence to the Gromovized Monge gap, but full GW optimization remains costly for long sequences.Source: A. Information Bottleneck and… · pp. 2–3

III. METHODOLOGY

The task is to predict continuous segment-level importance scores at valid positions while limiting contextual information and temporal-structure distortion. SGWIB processes modality-specific features through a backbone and subsequent structure-aware prediction components.

1) Problem Definition:

  • Highlight detection assigns continuous importance scores to valid temporal segments while suppressing task-irrelevant context and limiting distortion of temporal relational structure.The training formulation uses video indices, padded visual and audio sequences, segment-score sequences, validity masks, and sports-category indicators.Source: 1) Problem Definition · p. 3

2) Notation:

  • The notation section establishes that video indices are omitted in single-video module formulations and restored for dataset statistics and mini-batch aggregation..Source: 2) Notation · p. 3

B. Framework Overview

  • The framework extracts aligned features, decomposes them into task-oriented and context-oriented representations, compresses the task-oriented sequence stochastically, and predicts segment-level scores.SGMG compares detached source geometry with bottleneck geometry over one-dimensional projections; visual and audio branches use separate parameters and are optimized independently.Source: B. Framework Overview · p. 3

C. Backbone Module

  • The backbone preserves the input feature dimension, enabling direct comparison of input and bottleneck geometries; validity masks exclude padded positions from extraction, geometry construction, and loss computation.The temporal validity mask differs from the sports-supervision mask, which determines pseudo-environment supervision.Source: C. Backbone Module · p. 3

D. HAR-CPL-Guided Contextual Disentanglement

HAR-CPL-guided disentanglement targets sports-related contextual variation that may correlate with highlights without reliably indicating event importance. Pseudo-environment labels are derived from modality-specific contextual activity measures.

D. HAR-CPL-Guided Contextual Disentanglement

  • HAR-CDM separates highlight-useful information from contextual variation, using weak video-level HAR-CPL supervision for the context-oriented branch.Explicit pseudo-environment labels provide a weak signal for the home–away contextual factor, which task labels alone cannot expose in this setting.Source: D. HAR-CPL-Guided Contextual… · pp. 3–4

1) Sports-Video Selection and Pseudo-Environment Labels:

  • Sports videos are identified by their sports-category indicator, and the supervision mask selects that subset for pseudo-environment labeling.The resulting binary label represents relatively low or high contextual activity, not true home–away identity or segment-level highlight annotation.Source: 1) Sports-Video Selection and… · p. 4
  • Contextual activity is measured by audio feature energy and by average variation between adjacent valid visual features.The modality-specific threshold is estimated on sports training videos; the median is used because both measures are non-negative and right-skewed, to balance pseudo-environment sizes.Source: 1) Sports-Video Selection and… · p. 4

2) Contextual Representation Disentanglement:

HAR-CDM separates task-oriented and context-oriented representations, while SGWIB applies structure-aware compression to the task-oriented representation. SGMG measures excess relational distortion relative to an optimal sliced structural reference.

2) Contextual Representation Disentanglement:

  • HAR-CDM separates backbone features into task-oriented and context-oriented components, with orthogonality regularization applied to valid videos and pseudo-environment supervision limited to sports videos.The resulting task-oriented representation is passed to SGWIB, while the source-side geometry reference is detached.Source: 2) Contextual Representation… · pp. 4–5
  • The shared multi-head attention layer uses logits with opposite signs for the two branches; the task branch also receives a residual connection, whereas the context branch retains only attended content.Both branches share the value projection, so they differ in how they aggregate context.Source: 2) Contextual Representation… · p. 4

E. Sliced Gromov–Monge Gap

  • SGWIB replaces KL-based prior matching with SGMG, which measures excess relational distortion of the realized source-to-bottleneck mapping against an optimal sliced structural reference.The stochastic bottleneck uses one sampled channel scale per video, shared across temporal positions; inference uses its deterministic mean scale.Source: E. Sliced Gromov–Monge Gap · p. 5

1) Geometry Construction and Sliced Projection:

The geometry construction uses valid temporal positions and shared projections to compare source and bottleneck features while retaining temporal information in the structural representation.

1) Geometry Construction and Sliced Projection:

  • Only valid temporal positions enter the geometry construction, and their time coordinates are rescaled over the video's valid range to [−1, 1].Padded positions are excluded from the projection.Source: 1) Geometry Construction and Sliced… · p. 5
  • Stopgradient fixes the source geometry while gradients pass through the bottleneck and its upstream mapping, so the regularizer acts on the complete relational transformation.The source geometry is compared with the bottleneck geometry; the regularizer is not limited to the H_m → Z_m mapping.Source: 1) Geometry Construction and Sliced… · p. 5
  • The source and bottleneck features are augmented with a temporal coordinate weighted by α_time, then projected using the same fixed unit-norm directions.The shared projection direction makes each one-dimensional comparison reflect structural distortion under a shared view.Source: 1) Geometry Construction and Sliced… · p. 5

2) Mapping Cost and Sliced Structural Reference:

SGMG compares the relational cost of the prescribed mapping with an optimal sliced structural reference, then aggregates the resulting gap. Its information-theoretic interpretation is bounded to empirical sliced kernelized dependence under stated assumptions.

2) Mapping Cost and Sliced Structural Reference:

  • The mapping cost measures preservation of pairwise relational organization, while the one-dimensional structural reference uses the identity or reversed correspondence under equal cardinality, uniform weights, and squared-Euclidean costs.Sorting computes the reference only; it does not reorder the temporal sequence used for highlight prediction.Source: 2) Mapping Cost and Sliced Structural… · p. 5

3) SGMG Regularization:

  • SGMG aggregates projection-level gaps across videos with valid positions; for a video with one valid position its pairwise SGMG is zero, and videos with no valid positions are excluded.The gap is theoretically nonnegative under the exact one-dimensional assumptions, and the positive-part operator is retained for numerical robustness.Source: 3) SGMG Regularization · p. 6
  • A smaller SGMG indicates less excess relational distortion, and both sports and nonsports videos contribute whenever they contain valid temporal positions.The validity mask controls SGMG participation; the sports mask affects only pseudo-environment supervision.Source: 3) SGMG Regularization · p. 6

4) Information-Theoretic Interpretation:

  • Under equal cardinality, uniform empirical weights, bounded normalized projected supports, and kernel-cost consistency, expected SGMG controls an upper bound on empirical sliced kernelized dependence, not full Shannon mutual information.The proof applies the dependence bound to each projected pair and averages over projection directions.Source: 4) Information-Theoretic Interpretation · p. 6

F. SGMG-Based Information Bottleneck for Video Highlight Detection

SGWIB implements relevance through highlight regression and uses SGMG as a structure-aware compression surrogate. Its modality-specific objective combines the task loss with independently weighted regularizers.

F. SGMG-Based Information Bottleneck for Video Highlight Detection

  • Highlight relevance is implemented through segment-score regression, while SGMG regularizes compression rather than entering the scorer's forward path.The scorer takes Z_m as input.Source: F. SGMG-Based Information Bottleneck… · p. 6
  • The modality-specific objective combines the task loss with SGMG-based structural compression and HAR-CDM regularization, scaled by separate β_m and γ_m weights.The single HAR-CDM coefficient jointly scales its two terms.Source: F. SGMG-Based Information Bottleneck… · p. 6
  • Visual and audio branches have separate parameters and are trained independently, without cross-modal alignment or fusion.The dataset table is identified as a summary of the experimental datasets.Source: Table II · p. 7

IV. EXPERIMENTS

Experiments use MrHiSum and MoSu, with visual features as the primary comparison setting and audio evaluated independently. The study reports rank concordance and segment-level highlight retrieval metrics.

1) Datasets:

  • MrHiSum contains 31,892 videos and MoSu 52,678; both datasets derive from YouTube-8M and use YouTube Most Replayed statistics for annotation.MoSu averages 272.3 seconds per video; textual features are not used.Source: 1) Datasets · p. 7

2) Implementation Details:

  • The visual branch is the primary comparison setting, while audio is evaluated independently without visual–audio fusion or cross-modal interaction.The modality-specific backbone is CSTA, keeping the feature extractor fixed while evaluating the proposed regularizers.Source: 1) Datasets · p. 7, 2) Implementation Details · p. 7

3) Evaluation Metrics:

  • Kendall’s τ and Spearman’s ρ assess ranking concordance, while mAP@q measures retrieval after dividing videos into non-overlapping five-second segments.For mAP@q, the top q% of ground-truth segments are positives; the reported budgets are 15%, 30%, and 50%.Source: 3) Evaluation Metrics · p. 7

B. Comparison with State-of-the-Art Methods

SGWIB leads the visual-only benchmark comparisons on both datasets, and qualitative examples show predictions concentrated around annotated highlights. Ablations and bottleneck comparisons examine the contributions of HAR-CDM and SGMG-based regularization.

B. Comparison with State-of-the-Art Methods

  • Rank-based improvements are more pronounced than retrieval gains; SummDiff leads mAP@15 on MrHiSum, while CSTA scores 32.93 versus SGWIB’s 32.65 on MoSu.Method rankings also vary across benchmarks: SummDiff is second-best on MrHiSum but falls below CSTA and PGL-SUM on MoSu.Source: B. Comparison with State-of-the-Art… · p. 7
  • 0.192 τ, 0.255 ρ, 66.11 mAP@50, and 47.22 mAP@30 on MrHiSum; SGWIB ranks first on these four metrics across both datasets.Against second-best SummDiff on MrHiSum, the margins are 0.031 τ, 0.031 ρ, 0.87 mAP@50, and 0.75 mAP@30.Source: B. Comparison with State-of-the-Art… · p. 7, Table III · p. 8

C. Qualitative Results

  • 87.5% F1 on MrHiSum, 75.0% on MoSu, and 85.2% on a manually annotated YouTube video accompany more temporally localized predictions.The examples show SGWIB capturing multiple temporally separated highlight regions in the YouTube video.Source: C. Qualitative Results · p. 8

1) Component Ablation:

  • On MrHiSum Visual, the full model raises τ from 0.157 to 0.192 and ρ from 0.210 to 0.255; HAR-CDM and SGWIB each improve most settings.The independently trained audio models follow the same overall trend.Source: 1) Component Ablation · p. 8, Table IV · p. 9

2) Comparison of Information Bottleneck Formulations:

  • Full-GW prediction results are not directly comparable because the method’s substantially higher cost restricts GWIB to the efficiency analysis.The comparison includes conventional KL-based IB, full GWIB, and SGWIB with the SGMG regularizer.Source: 2) Comparison of Information… · pp. 8–9
  • SGWIB outperforms KL-based bottleneck regularization across both datasets and modalities, improving all five visual metrics on each dataset.Audio-branch gains in τ and ρ are 0.013 and 0.019 on MrHiSum and 0.012 and 0.016 on MoSu.Source: 2) Comparison of Information… · p. 9

3) Computational Efficiency Analysis:

SGWIB retains nearly KLIB’s training efficiency while preserving temporal structure under compression more effectively. The structural analysis compares the methods’ effects on segment representations and their relational geometry.

3) Computational Efficiency Analysis:

  • SGWIB takes 20m 34s per epoch versus 18m 40s for KLIB, a 10.2% overhead; full GWIB takes 2h 15m 58s, or 7.28× KLIB’s cost.The full GWIB schedule is excluded from task-level comparison because running it across every dataset and modality is prohibitively expensive.Source: 3) Computational Efficiency Analysis · p. 9, Table VI · p. 10

4) Structural Preservation Analysis:

  • The structural diagnostic compares input and bottleneck representations for a representative MrHiSum video containing 98 valid segments.It evaluates pairwise distortion, paired shift, and centroid shift in a standardized feature space.Source: 4) Structural Preservation Analysis · p. 9
  • At βv = 10−6, KLIB’s pairwise distortion rises from 0.0076 to 0.0577, paired shift from 3.11 to 24.12, and centroid shift from 2.99 to 23.35.At βv = 10−1, the three KLIB metrics reach 0.1077, 33.95, and 32.35, respectively.Source: 4) Structural Preservation Analysis · pp. 9–10
  • Across the tested βv range, SGWIB keeps bottleneck vectors adjacent to their source segments, while KLIB’s compressed segments move together in long parallel trajectories.The t-SNE visualization is a qualitative complement because it does not strictly preserve global distances.Source: 4) Structural Preservation Analysis · pp. 9–10

5) Sensitivity to the Bottleneck Coefficient:

The bottleneck coefficient controls the balance between highlight prediction and structure-aware compression, while the HAR-CDM loss weight has a separate sensitivity pattern. The conclusion describes SGWIB as combining structural preservation and contextual disentanglement, with consistent improvements on two datasets.

5) Sensitivity to the Bottleneck Coefficient:

  • The visual branch performs best around βv = 10−6 on four of five metrics, while audio peaks at βa = 10−8 on all five.At β = 10−6, visual performance is maximal and audio performance minimal on every metric; excessive compression suppresses highlight-relevant information.Source: 5) Sensitivity to the Bottleneck… · p. 10

6) Sensitivity to the HAR-CDM Loss Weight:

  • At γv = 0.01, all five visual metrics peak; audio optima vary by metric, and both modalities degrade sharply at γm = 0.05.The paper favors a moderate HAR-CDM weight for both modalities, with audio tolerating a wider range.Source: 6) Sensitivity to the HAR-CDM Loss… · p. 11

V. CONCLUSION

  • On MrHiSum and MoSu, SGWIB improves temporal ranking and highlight retrieval; ablations show complementary effects of HAR-CDM and SGWIB.Compared with KLIB, SGWIB achieves better task performance and substantially stronger structural preservation with modest additional computational cost.Source: §V · p. 11
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