# ShotBench: expert-level cinematic understanding benchmark for VLMs

- id: src_01M569C8FD9EVBATHE7NEZXYZE
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T02, T20 · tags: level:frontier, benchmark, vlm, cinematography, cinematography-understanding, cc-by
- updated: 2026-10-18T01:17:49.921Z
- url: https://veyrul.com/sources/src_01M569C8FD9EVBATHE7NEZXYZE

Source: https://arxiv.org/abs/2506.21356

L07 2026-10-17: merged duplicate records from x03:arxiv-2506-21356-shotbench (same canonical URL).

```json
{
  "archived": null,
  "checked_at": "2026-10-17T00:00:00.000Z",
  "parent_url": null,
  "usefulness": "3.5k+ expert QA pairs from 200+ acclaimed films across eight cinematography dimensions; measures whether VLMs can name shots, lighting and camera work correctly.",
  "source_type": "paper",
  "availability": "available",
  "dependencies": [],
  "source_owner": "Liu et al. (Vchitect)",
  "next_check_at": "2026-10-31T00:00:00.000Z",
  "successor_url": null,
  "hardware_notes": null,
  "known_limitations": [
    "The eight dimensions are not named on the abstract page; read the paper before citing them."
  ],
  "upstream_updated_at": "2025-06-27T00:00:00.000Z"
}
```

Evidence claims:
- [sourced_fact] Abstract: over 3.5k expert-annotated QA pairs from images and clips, curated from 200+ films (predominantly Oscar-nominated), spanning eight dimensions. · locator: abstract · source: https://arxiv.org/abs/2506.21356
- [upstream_claim] Abstract: evaluation of 24 leading VLMs reveals substantial limitations. · locator: abstract · source: https://arxiv.org/abs/2506.21356
- [sourced_fact] Repository Vchitect/ShotBench has no license file per GitHub; last push 2025-09-12. · locator: repository metadata · source: https://github.com/Vchitect/ShotBench
- [upstream_claim] Over 3,500 expert-annotated QA pairs from 200+ films spanning eight cinematography dimensions; 24 models tested; best under 60% average accuracy; ShotQA (~70K pairs) and ShotVL released. · locator: abstract · source: https://arxiv.org/abs/2506.21356
- [sourced_fact] Accepted at NeurIPS 2025; CC BY 4.0 on arXiv. · locator: comments and license · source: https://arxiv.org/abs/2506.21356
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.

Rights:
- documentation: license=CC-BY-4.0 hosting=permitted redistribution=permitted modification=permitted training=unknown · arXiv page lists CC BY 4.0 for the paper. · evidence: https://arxiv.org/abs/2506.21356
- code: license=unknown hosting=unknown redistribution=unknown modification=unknown training=unknown · No license file detected. · evidence: https://github.com/Vchitect/ShotBench
- dataset: license=unknown hosting=unknown redistribution=unknown modification=unknown training=unknown · Benchmark content is drawn from films; dataset license not checked.

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