# Which automatic metric does T2I-CompBench++ use for each compositional category (colour/shape/texture binding, 2D/3D spatial, numeracy, non-spatial, complex), and how well does each correlate with human ratings?

- id: art_01M5NP1XCXKMNRK0CNSP13C18J
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T05, T20 · tags: none
- updated: 2026-10-24T00:47:58.872Z
- url: https://veyrul.com/articles/art_01M5NP1XCXKMNRK0CNSP13C18J

Partially verified: the curator re-read none of the cited sources; S3, S4, S5 rest on the producer's pinned reading. T2I-CompBench++ (arXiv v3) has 8,000 prompts in four groups and eight sub-categories, and uses a different checker per sub-category: disentangled BLIP-VQA for colour/shape/texture binding, a UniDet detection (plus depth for 3D) rule for spatial relations and numeracy, CLIPScore or an MLLM for non-spatial relations, and a 3-in-1 average for complex prompts. Validated by Kendall tau / Spearman rho against 3-worker MTurk ratings, B-VQA is strongest for colour (tau 0.63) but weak for shape (0.27), UniDet is the best non-MLLM metric for spatial and numeracy, and plain CLIPScore is weak almost everywhere.

Partially verified: the curator re-read none of the cited sources; S3, S4, S5 rest on the producer's pinned reading.

T2I-CompBench++ (arXiv v3) has 8,000 prompts in four groups and eight sub-categories, and uses a different checker per sub-category: disentangled BLIP-VQA for colour/shape/texture binding, a UniDet detection (plus depth for 3D) rule for spatial relations and numeracy, CLIPScore or an MLLM for non-spatial relations, and a 3-in-1 average for complex prompts. Validated by Kendall tau / Spearman rho against 3-worker MTurk ratings, B-VQA is strongest for colour (tau 0.63) but weak for shape (0.27), UniDet is the best non-MLLM metric for spatial and numeracy, and plain CLIPScore is weak almost everywhere.

Curator qualifications: Partially verified: the curator re-read none of the cited sources; S3, S4, S5 rest on the producer's pinned reading. Curator GET 2026-10-23 of the sources not re-read (mechanical source evidence, not semantic verification): S3: HTTP 200, bytes differ from the producer pin; S4: HTTP 200, bytes differ from the producer pin; S5: HTTP 200, bytes differ from the producer pin.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2307.06350v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/abs/2307.06350v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/Karine-Huang/T2I-CompBench/tree/4aa404212eb5d06e5adbcd9cee696c750d0d25a5",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] T2I-CompBench++ (arXiv v3) has 8,000 prompts in four groups and eight sub-categories, and uses a different checker per sub-category: disentangled BLIP-VQA for colour/shape/texture binding, a UniDet detection (plus depth for 3D) rule for spatial relations and numeracy, CLIPScore or an MLLM for non-spatial relations, and a 3-in-1 average for complex prompts. Validated by Kendall tau / Spearman rho against 3-worker MTurk ratings, B-VQA is strongest for colour (tau 0.63) but weak for shape (0.27), UniDet is the best non-MLLM metric for spatial and numeracy, and plain CLIPScore is weak almost everywhere. · locator: candidate evidence excerpts · source: https://arxiv.org/abs/2307.06350v3

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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