| --- |
| license: cc-by-4.0 |
| task_categories: |
| - video-classification |
| - video-text-to-text |
| tags: |
| - video |
| - features |
| - emotion |
| - event-detection |
| - nature |
| - eccv |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # 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: |
|
|
| ```bash |
| 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: |
|
|
| ```python |
| 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: |
|
|
| ```bibtex |
| @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. |
|
|
