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TRINITY: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection

Qianqian Chen, Hyun Bin Kim, Denzel Elden Wijaya, Yang Yi, Bo Liu, Yangkai Ding

arXiv:2608.29577v1cs.CV

TL;DR

Video highlight detection is limited by event-centric formulations that do not adequately represent heterogeneous saliency in unconstrained personal videos. TRINITY introduces a three-perspective benchmark and a shared-backbone multi-branch model with view-specific experts, achieving strong performance across benchmarks. The authors conclude that multi-perspective modeling supports a more comprehensive formulation of video saliency.

  • Problem

    Existing highlight-detection benchmarks and methods largely use narrow, event-centric saliency definitions, limiting coverage of heterogeneous personal-video highlights.

  • Method

    TRINITY decomposes saliency into Event, Emotion, and Nature and uses a shared-backbone multi-branch architecture with perspective-specific experts.

  • Results

    The model achieves strong performance on TRINITY and state-of-the-art performance on existing event-centric and emotion-centric benchmarks.

  • Takeaways & Limitations

    Multi-perspective modeling provides a more structured formulation of highlight detection across heterogeneous highlight definitions.

  • Takeaways & Limitations

    Rare emotions and direct external validation remain future work.

Abstract

from arXiv · show

Traditional video highlight detection relies on a narrow, event-centric definition of saliency, which often fails to generalize to unconstrained personal videos where highlights are heterogeneous and perspective-dependent. To address this, we introduce TRINITY, a multi-perspective benchmark that decomposes highlight saliency into three complementary dimensions, Event, Emotion, and Nature, within a unified temporal framework. Leveraging this multi-faceted view, we propose a shared-backbone multi-branch architecture designed for parallel multi-perspective prediction via view-specific experts. Comprehensive experiments demonstrate that our method significantly outperforms state-of-the-art baselines, achieving gains of +7.15/+3.62 mAP (rho=15%/50%) on Mr. HiSum and +10.82 mAP on YouTube Highlights. These results validate that multi-perspective modeling provides a more robust and comprehensive formulation of video saliency, especially for complex real-world scenarios. The benchmark and relevant codes will be released upon acceptance. The benchmark is available at https://huggingface.co/datasets/vanilladucky/TRINITY and the code is available at https://github.com/vanilladucky/TRINITY.

1 Introduction

TRINITY reframes highlight detection as multi-perspective saliency modeling for personal videos, decomposing highlights into Event, Emotion, and Nature. Its shared-backbone multi-branch model predicts these perspectives in parallel and achieves strong performance across diverse benchmarks.

  • Motivation: Existing benchmarks largely define highlights around salient events or narrative peaks, limiting the conceptual scope of saliency.This narrow formulation often struggles with unconstrained personal videos, where salient moments are subtle and heterogeneous.
  • Motivation: Personal-video highlights can arise from affective expressions, social interactions, or scenery, making saliency multi-causal and perspective-dependent.A single video may contain both scenic and emotional highlights.
  • TRINITY: TRINITY decomposes highlight saliency into complementary Event, Emotion, and Nature dimensions within one temporal framework.Emotion and Nature use scalable automatic annotation, while Event follows established event-centric annotations.
  • Architecture: The proposed shared-backbone multi-branch architecture uses view-specific experts to localize highlights for each perspective while modeling global temporal context.This factorization supports parallel prediction from a single input sequence.
  • Results: The model establishes a strong TRINITY baseline and achieves state-of-the-art results on event-centric and emotion-centric benchmarks.The experiments also include ablations validating the proposed components.

2 Related Work

Related highlight-detection methods include query-conditioned retrieval and text-agnostic saliency prediction. However, existing approaches generally optimize a single saliency notion, motivating unified modeling of heterogeneous visual highlight patterns without textual conditioning.

  • Existing paradigms: Query-conditioned methods formulate highlight detection as query-guided moment retrieval with clip-level saliency prediction.They use textual queries at inference time to anchor saliency to user intent.
  • Existing paradigms: Text-agnostic methods predict saliency directly from visual and audio cues using edited-video supervision, ranking objectives, or audio–visual modeling.Recent work also emphasizes cross-category generalization.
  • Research gap: Most existing approaches implicitly optimize a single, typically event-driven notion of saliency rather than heterogeneous highlight patterns.This motivates complementary visual perspectives under a shared temporal protocol.
  • Benchmarking: Highlight benchmarks use multiple annotation strategies, including segment labels, aggregated shot importance, user-created GIFs, and personalized histories.These datasets provide different forms of supervision for subjective highlight detection.

3 TRINITY Dataset

TRINITY constructs a three-perspective highlight benchmark—Event, Emotion, and Nature—using distinct annotation procedures within a unified temporal framework. Its perspectives exhibit different temporal characteristics, supporting multi-perspective modeling of personal-video saliency.

  • Dataset Overview: TRINITY organizes highlights into Event, Emotion, and Nature perspectives over open-sourced videos.Event annotations adopt replay-based supervision, while Emotion and Nature receive newly constructed annotations.
  • Event Perspective: Event labels use aggregated Most Replayed statistics as a proxy for temporally localized semantic events attracting collective viewer attention.Segments with consistently high replay intensity are treated as event-driven highlights.
  • Emotion Perspective: Emotion labels combine stable non-neutral facial-expression screening with multimodal context verification, retaining segments only when both stages agree.Frames are sampled at 1 fps, and candidate segments require at least three consecutive frames with the same non-neutral category before Stage-2 verification.
  • Emotion Perspective: 92% inter-annotator agreement and 94% majority-vote correctness support the consistency of the emotion annotations.The evaluation used three independent annotators on 50 randomly sampled annotated segments.
  • Nature Perspective: Nature annotations identify scenic segments through semantic filtering and assign frame-level aesthetic scores as regression targets.The pipeline combines scenic localization with aesthetic assessment, retaining supervision only in regions satisfying scenic alignment and aesthetic-quality criteria.
  • Statistical Overview: Emotion highlights are typically short and concentrated at approximately 4.87 s, whereas Nature highlights have longer temporal characteristics.The reported statistics show domain and temporal distinctiveness across the perspectives.

4 Methodology

The methodology formulates highlight detection as three parallel temporal scoring tasks supported by shared global context and perspective-specific temporal experts. Separate prediction heads produce Event, Emotion, and Nature highlight probabilities from fused shared and private representations.

  • Overall Architecture: The model splits each video into non-overlapping 5-second segments and predicts Event, Emotion, and Nature highlight scores.Emotion uses binary labels, while Event and Nature use continuous labels derived from replay statistics and aesthetic scores.
  • Temporal Backbones: A shared temporal Transformer captures task-agnostic global context, while three private Transformers specialize in perspective-specific temporal patterns.The backbones explicitly model ordered segments and inject relative temporal information with 1D RoPE.
  • Representation Factorization: Distinct query, key, and value projections encourage shared and task-private representations to occupy complementary latent regions.This factorization supports separate modeling of global context and task-specific temporal cues.
  • Prediction Heads: Each task concatenates shared and private embeddings before an independent prediction head maps the fused representation to a highlight probability.The heads use multiple linear layers with ReLU activations and sigmoid scoring.
  • Training Strategy: Deterministic task-conditioned routing sends each task batch to its private backbone and the shared backbone, with gradients accumulated after three round-robin mini-batches.This trains each private backbone on its own supervision while exposing the shared backbone to all tasks.
  • Optimization: The training objective sums the three binary cross-entropy task losses with equal weights.The weighting coefficients satisfy λ_e = λ_m = λ_n = 1.
  • Inference: At inference, shared and perspective-specific outputs are fused separately for each task to produce multi-perspective scores for every temporal segment.The resulting predictions include Event, Emotion, and Nature highlight scores.

5 Experiments

Experiments evaluate TRINITY pretraining, public-benchmark transfer, multi-perspective performance, architectural choices, dataset supervision, gradient interference, and temporal attention specialization.

  • Experimental Setup: TRINITY pretraining groups 27,845 Event, 16,257 Emotion, and 15,540 Nature videos, using joint multidataset training across perspectives.Evaluation fine-tunes only the Event branch for public event-centric benchmarks while freezing other components and removes overlapping Mr. HiSum test videos from pretraining.
  • Public Benchmarks: 40.98 mAPρ=15% and 69.06 mAPρ=50% on Mr. HiSum exceed SummDiff by +7.15 and +3.62 points, respectively.On YouTube Highlights, the method reaches an average score of 83.82, exceeding PLD-VHD by +10.82 points; VEATIC performance is 0.8185 SAGR and 0.1749 RMSE.
  • TRINITY Evaluation: TRINITY evaluation gives the best overall performance across Nature, Emotion, and Event under stricter evaluation ratios.Nature reaches 58.74 mAPρ=15% and 68.05 mAPρ=50%; Emotion reaches 67.42 and 71.32; Event reaches 40.98 and 69.06.
  • Architecture Ablation: +3.50 mAPρ=15% results from moving from single-task training to shared-backbone multi-task learning, while shared–private experts achieve 55.71 / 69.48 overall.Relative to independent experts, the shared–private design improves Event from 37.54 to 40.98, Nature from 44.78 to 58.74, and Emotion from 63.27 to 67.42 at mAPρ=15%.
  • Dataset Contribution Ablation: Adding Nature supervision raises Nature performance from 31.25 to 57.89 at mAPρ=15%, while all three perspectives produce the highest overall average of 55.71 / 69.48.The reported results indicate limited negative interference when adding Nature supervision and the largest final gain on Emotion.
  • Analysis: Gradient cosine similarity stabilizes near zero after early training, indicating limited gradient interference, and attention localizes distinct temporal cues for Emotion, Event, and Nature.The visualized heads prioritize facial expressions, physical transitions, and landscape vistas, respectively, with low cross-correlation between head-specific distributions.

6 Conclusion

The paper reframes personal-video highlight detection around heterogeneous saliency and introduces TRINITY with a shared-backbone multi-expert architecture. Experiments report strong TRINITY performance and consistent gains on existing benchmarks.

  • Conclusion: The conclusion identifies event-centric formulations as limited for realistic personal-style videos and introduces TRINITY’s Event, Emotion, and Nature dimensions.The benchmark decomposes highlight saliency within a multi-perspective formulation.
  • Conclusion: A shared-backbone multi-expert architecture models perspective-specific saliency while experiments report consistent gains across heterogeneous highlight definitions.The conclusion frames the results as improved generalization across heterogeneous highlight definitions.

A Details of Collection and Annotation of TRINITY

TRINITY constructs its three perspective groups using different collection strategies tailored to annotation difficulty. Event annotations derive from Mr. HiSum, while Emotion and Nature use publicly available video datasets and direct visual cues.

  • Event Perspective: The Event perspective is derived entirely from Mr. HiSum because reliable large-scale annotation requires semantically meaningful events with collective attention signals.Mr. HiSum replay statistics provide the basis for event-driven annotations.
  • Collection Strategy: TRINITY’s source video pool consists of ten widely used public datasets used to construct the Emotion and Nature perspectives.The source pool includes datasets such as QuerYD, TACoS, Mr. HiSum, DiDeMo, InternVid-VTime, CosMo-Cap, QVHighlights, and Charades-STA.
  • Emotion and Nature Perspectives: Emotion and Nature highlights are annotated more directly from visual cues such as facial expressions and scenic aesthetics.These perspectives are therefore constructed from a large pool of publicly available video datasets.

A.2 Cross-Perspective Overlap and Orthogonality Analysis

Cross-perspective overlap analysis finds very small temporal intersections, indicating that Event, Emotion, and Nature capture largely independent highlight structures.

  • Overlap Statistics: Event–Emotion overlap is 0.034 IoU across 3,096 videos, with Emotion peaks shorter and less temporally covered than Event segments.Emotion averages 4.35 seconds and 5.0% coverage, compared with 10.84 seconds and 15.5% for Event.
  • Overlap Statistics: Emotion–Nature overlap is 0.002 IoU, with only 78 videos containing valid annotations for both perspectives.The reported overlap is negligible within the analyzed shared video subset.
  • Orthogonality Analysis: The analysis concludes that the three perspectives provide complementary saliency signals with substantially independent temporal structures.This supports treating the perspectives as largely orthogonal rather than covering the same highlight segments.

A.3 Emotion Annotation Statistics

Emotion-driven highlights are heavily concentrated in Happiness, while rarer negative and specific emotions form a long tail that complicates robust detection across affective expressions.

  • Happiness accounts for 86.55% of annotated emotion-driven highlight peaks.
  • Sadness, Anger, and Disgust occur less frequently, while Fear and Surprise appear in only a small number of segments.
  • The emotion distribution follows a long-tail pattern in which a few common categories dominate and rarer categories remain sparsely represented.
  • This imbalance creates a challenge for robust emotion-driven highlight detection across diverse affective expressions.

A.4 Checking of Annotations

The annotation audits evaluate threshold choices, emotion-label reliability, scenic localization, and aesthetic highlight validity using human judgments and agreement measures. Results indicate strong consistency, while landscape thresholds require balancing inclusiveness against strictness.

  • Landscape threshold: A manually audited subset contains 80 scenic and 20 non-scenic videos, with accuracy computed across all 100 videos.
  • Landscape threshold: A landscape threshold that is too small admits videos with little scenic content, weakening dataset purity, whereas a threshold that is too large rejects genuinely scenic videos.
  • Human auditing: The annotation procedure uses majority voting across three annotators for emotion labels, scenic frames, and aesthetic highlight validation.
  • Emotion annotation consistency: 94% of sampled emotion labels were verified as correct by majority vote, with 92% inter-annotator agreement.
  • Scenic validity: Scenic localization achieved average label-human IoU of 0.89727 and average inter-annotator IoU of 0.89744.
  • Aesthetic ordering: The aesthetic-highlight audit concludes that model-selected highlights are largely consistent with human judgment and that annotators themselves show high agreement.
  • Loss-weight checking: Uniform loss weighting provided more stable convergence and superior performance for this architecture than evaluated dynamic weighting alternatives.
  • Loss-weight checking: Changing the event loss weight to λ = 2 or λ = 0.5 produced no significant result changes, reducing the need for tedious tuning.

B.2 Training methods

The training analysis compares sequential and round-robin optimization strategies for multi-task learning. Sequential training can cause catastrophic forgetting in a shared backbone, while private task-specific backbones preserve performance more effectively.

  • Compared methods: The comparison evaluates a single transformer backbone with three heads and the proposed architecture under alternative sequential and round-robin training procedures.
  • Training strategy: Sequential training of a single shared backbone performs very poorly compared with the proposed method because tasks overwrite knowledge from previous tasks.
  • Training strategy: Round-robin training substantially improves results for the single-backbone alternative compared with sequential training.
  • Private backbones: The proposed architecture shows only minimal degradation under sequential training on the stricter mAPρ=15% metric.
  • Private backbones: Private backbones retain task-specific information while the shared backbone contributes global information during sequential training.

B.3 Analysis on View Co-occurrence and MixBatch Strategy

The analysis tests TRINITY under dense view co-occurrence, alternative batching, and representative video scenarios. The model maintains perspective-specific behavior in overlapping settings, while mixed-perspective batches substantially hurt Nature performance.

  • View co-occurrence: On a strict 78-video subset containing valid Event, Emotion, and Nature annotations, the model maintains robust performance across all perspectives.
  • MixBatch strategy: MixBatch yields marginal gains on TRINITY-Emotion and TRINITY-Event but reduces Nature mAP15 from 58.74% to 42.19%.
  • Qualitative cases: The four qualitative cases cover skateboarding, leaf-flower crafting, indoor dance, and tool-making videos with different combinations of action, emotion, and scenery.
  • Qualitative cases: The qualitative examples show that perspective-specific experts respond selectively to each video's semantic content.
  • Qualitative cases: In skateboarding, Event captures attempts and Emotion responds to facial reactions, while Nature remains inactive because scenic content is absent.
  • Qualitative cases: In the crafting vlog, Nature highlights the completed flower opening, Event captures crafting actions, and Emotion remains inactive because faces are absent.
  • Qualitative cases: In indoor dance and tool-making videos, Event remains active, whereas Nature is inactive without scenery and Emotion is inactive when faces are absent.
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