TRINITY Dataset

TRINITY accompanies our ECCV 2026 paper "TRINITY: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection" and provides precomputed video features for three complementary highlight-detection perspectives: Emotion, Event, and Nature.

Traditional highlight detection assumes a single, event-centric notion of saliency, which fails to generalize to unconstrained personal videos where highlights are heterogeneous and perspective-dependent. TRINITY decomposes highlight saliency into three complementary dimensions within a unified temporal framework:

• Event — event-driven semantic peaks, adopting the replay-based annotations from Mr. HiSum.
• Emotion — affective facial moments, annotated via a scalable two-stage automatic pipeline (frame-level facial expression recognition + multimodal LLM verification).
• Nature — aesthetic scenic highlights, annotated via scenic localization (person/landscape detection) followed by frame-level aesthetic scoring. Features are sampled at 1 FPS, same as Event.

The three perspectives are intentionally complementary rather than redundant (cross-perspective IoU as low as 0.002–0.052 on Mr. HiSum), enabling structured analysis of heterogeneous highlight patterns beyond the traditional single-perspective paradigm. This repository provides only the precomputed CLIP ViT-B/32 features (and Emotion ground-truth labels) used in the paper's experiments — see the paper for full annotation pipeline details.

Subsets

Subset	Feature granularity	Feature dim	Dtype	Ground truth
Emotion	5-second averaged windows	512	float16	binary highlight labels + segment metadata (separate archive)
Event	1 FPS	512	float32	frame-level replay-saliency scores (separate archive)
Nature	1 FPS	512	float16	frame-level aesthetic scores (separate archive); includes _aug augmented variants

All features are stored per-clip as individual .npz files, packaged into numbered zip shards for download.

Emotion

• emotion/emotion_feature_{1..4}.zip — per-clip feature files, each .npz with key features of shape (T, 512).
• emotion/emotion_gt.zip — matching ground-truth files, each .npz containing:
  • labels: shape (T,), float32
  • clip_start, clip_end, clip_duration: clip timing info
  • num_segments, segment_duration: segmentation info
  • is_negative: bool flag for negative samples
• emotion/emotion_split.json — train_keys / val_keys / test_keys listing the relative .npz filenames for each split (11,352 / 1,615 / 3,290 clips).

Feature and ground-truth files share the same filename across the two archive sets.

Event

• event/event_features_part_{1..145}.zip — per-video feature files, each .npz with key data of shape (T, 512), float32, sampled at 1 FPS.
• event/event_gt_part_1.zip — matching ground-truth files, each .npz with key data of shape (T,), float64, giving the frame-level replay-based saliency score (one value per 1-second frame, aligned with the feature sequence).
• event/mr_hisum_split_original.json — train_keys / val_keys / test_keys (27,892 / 2,000 / 2,000 videos).

Feature and ground-truth files share the same filename (e.g. video_1.npz) across the two archive sets.

Nature

• nature/nature_feat_part_{1..32}.zip — per-video feature files, each .npz with key features of shape (T, 512), sampled at 1 FPS. Files suffixed _aug are augmented variants of their base video.
• nature/nature_gt_part_1.zip — matching ground-truth files, each .json containing a list of per-frame aesthetic saliency scores in [0, 1], aligned with the feature sequence.
• nature/nature_split.json — train_keys / val_keys / test_keys (12,763 / 874 / 1,903 clips).

Feature and ground-truth files share the same base filename (e.g. --rd0X_LQCo.npz / --rd0X_LQCo.json) across the two archive sets.

Usage

Download and unzip the shards for the subset(s) you need:

hf download vanilladucky/TRINITY --repo-type dataset --local-dir ./TRINITY
cd TRINITY/nature && for f in *.zip; do unzip -q "$f" -d nature_feat; done

Load a feature file:

import numpy as np
feat = np.load("nature_feat/--rd0X_LQCo.npz")["features"]  # (T, 512)

Citation

If you use this dataset, please cite our paper:

@inproceedings{chen2026trinity,
  title     = {{TRINITY}: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection},
  author    = {Chen Qianqian and Kim Hyun Bin and Wijaya Denzel Elden and Yi Yang and Liu Bo and Ding Yangkai},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}

License

Released under CC BY 4.0. Underlying source videos belong to their original datasets/owners (e.g. Mr. HiSum, YouTube); this release distributes only extracted features and derived annotations.