| --- |
| license: cc-by-4.0 |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - vlm-evaluation |
| - metric-estimation |
| - counterfactual-interventions |
| - spatial-reasoning |
| pretty_name: Metric VQA |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: itw |
| data_files: itw/questions.jsonl |
| - config_name: objectron |
| data_files: objectron/questions.jsonl |
| --- |
| |
| # Metric VQA |
|
|
| A two-split benchmark for diagnosing evidence use in vision-language models on |
| monocular metric object-size estimation: given a single RGB image and a |
| natural-language dimension query, predict the physical dimension in |
| centimeters. |
|
|
| - **In-the-wild (ITW):** 331 queries over 70 cluttered scene photographs, |
| every ground-truth dimension measured by tape measure (0.8–245 cm, |
| ~99 distinct objects). Each query carries a human-verified instance mask. |
| - **Objectron:** 10,482 queries over 3,494 frames from ~2,600 object |
| instances in 9 categories of Google Objectron, ground truth from the |
| dataset's oriented 3D bounding boxes. |
|
|
| The benchmark is a diagnostic instrument, not a leaderboard: the companion |
| intervention suite (object-only, object-ring, wrong-object, random-mask, |
| zoom, lens distortion, text-only) probes which evidence channels a model's |
| estimate actually depends on. |
|
|
| **Paper:** [Ill-Posed by Design: Probing Evidence Use in VLMs (arXiv:2606.24335)](https://arxiv.org/abs/2606.24335) · **Code:** https://github.com/bmeivar/MetricVQA |
|
|
| ## Layout |
|
|
| ``` |
| itw/questions.jsonl 331 records (schema below) |
| itw/questions.csv same records, flat CSV |
| itw/images/ 70 scene photographs (PNG) |
| itw/masks/ 331 binary instance masks (PNG, one per query) |
| objectron/questions.jsonl 10,482 records |
| objectron/questions.csv same records, flat CSV |
| ``` |
|
|
| Objectron frames are distributed separately (see below) to respect the |
| source dataset's distribution terms; the extraction script reproduces them |
| deterministically from the public Objectron release. |
|
|
| ## Record schema (JSONL, one object per line) |
|
|
| | field | type | splits | description | |
| |---|---|---|---| |
| | `query_id` | str | both | stable id, `itw_0001` / `obj_00001` | |
| | `split` | str | both | `itw` or `objectron` | |
| | `image` | str | both | path relative to the split directory | |
| | `question` | str | both | verbatim query; at evaluation time it is followed by the instruction *"Answer with a single float number in centimeters"* | |
| | `answer_cm` | float | both | ground-truth dimension in centimeters | |
| | `axis` | str | both | `length` / `height` / `width` / `depth` / `diameter` / `size`, parsed from the query | |
| | `target_phrase` | str | itw | referring expression grounding the query to one object | |
| | `mask` | str | itw | binary PNG instance mask for the target | |
| | `bbox_xyxy` | [int×4] | itw | tight box of the mask, pixel coordinates | |
| | `mask_source` | str | itw | `sam3_auto_verified` (SAM 3 from the phrase, passed human review) or `manual_box_prompt` (re-extracted from a human-drawn box) | |
| | `gt_method` | str | both | `tape_measure` or `objectron_3d_box` | |
| | `category` | str | objectron | one of the 9 Objectron categories | |
|
|
| ## Annotation process (ITW) |
|
|
| Scenes were photographed and measured by the authors. Target masks were |
| initialized with SAM 3 from each query's referring phrase; a human annotator |
| reviewed all 331 masks and re-extracted those that failed review (32 objects, |
| covering 53 queries) using manually drawn box prompts, so every mask in the |
| release is human-verified. The `mask_source` field preserves provenance per |
| query. |
|
|
| ## Evaluation protocol |
|
|
| Primary metric: **Acc@10%** — fraction of queries with |ŷ−y|/y ≤ 0.1. |
| Responses with no parsable number are excluded from accuracy denominators |
| (a parse failure is an instruction-following failure, not a size estimate); |
| report parse rates alongside. Paired intervention deltas are computed on |
| rows parsable in both conditions. Significance: scene-clustered sign-flip |
| permutation tests with Benjamini–Hochberg correction (queries sharing a |
| scene flip together). |
|
|
| ## Licensing |
|
|
| - ITW images, masks, and annotations: **CC-BY-4.0**. |
| - Objectron-derived annotations: released under the terms of the |
| [Objectron dataset license](https://github.com/google-research-datasets/Objectron), |
| with attribution; frames are re-derivable from the public source. |
| - Evaluation and intervention code: **MIT**. |
|
|
| ## Intended use and limitations |
|
|
| Intended for diagnosing evidence use and for benchmarking metric estimation |
| under monocular ambiguity. The ITW split is evaluation-only. Ground truth is |
| a single tape measurement per dimension; the human-reference row reported in |
| the paper is a single annotator, not a population estimate. Queries are in |
| English. |
|
|
