license: cc-by-nc-sa-4.0
language:
- en
size_categories: - 100K<n<1M
Dataset Card for SafeVid‑350K
Dataset Summary
SafeVid‑350K is a large‑scale, video‑centric safety alignment dataset designed to instill Helpful, Honest, Harmless (3H) principles in Video Large Multimodal Models (VLMMs). It contains 350 000 preference pairs (chosen vs. rejected responses) covering 30 scene categories and 29 fine‑grained safety sub‑dimensions. Each example links to a YouTube video segment via YoutubeID + start/end timestamp, includes a rich textual description, and provides adversarial questions explicitly crafted to probe safety vulnerabilities.
Supported Tasks
| Task | Description | Metrics |
|---|---|---|
| Safety‑aligned response generation | Given a video segment + adversarial question, produce a safe, helpful answer | Safety Rate |
Dataset Structure
Data Fields
| Field | Type | Description |
|---|---|---|
YoutubeID |
string |
YouTube video ID |
Start_timestamp / End_timestamp |
string |
"HH:MM:SS.mmm" segment boundaries |
Caption |
string |
Video auto‑caption (original language) |
Aesthetic_Score |
float |
Aesthetic quality from CLIP‑based predictor |
UMT_Score |
float |
Temporal saliency score from UMT |
Language |
string |
ISO‑639‑1 code of caption language |
Type |
string |
Original InternVid top‑level category |
description |
string |
GPT‑4–refined dense narration (≈100 words) |
⟨3H‑subcategory⟩ |
list[string] |
Adversarial questions for that safety dimension |
Response_pairs |
dict |
“rejected_response vs. chosen_response” for each question |
⚠️ Video bytes are not redistributed; only metadata + IDs are included to respect YouTube TOS.
File Layout
data/ # 30 JSON files, one per scene
├─ man-made environment_building_business_filtered.json
├─ natural environment_land_beach_filtered.json
⋯