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license: cc-by-4.0
---
# VistaQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence
## Overview
VistaQA is a benchmark for the joint evaluation of free-form answer correctness and pixel-level visual evidence alignment in visual question answering. It contains 1,157 expert-curated samples across six task types and six visual domains, spanning perception to compositional and relational reasoning. Each sample requires both a textual answer and corresponding segmentation masks that support the prediction. The benchmark also includes hallucination-aware samples in which no valid visual evidence exists.
---
## Dataset Structure
```
VistaQA/
├── 1.jpg
├── 1.json
├── 2.jpg
├── 2.json
└── ...
```
Each image file (`.jpg` or `.png`) is paired with a corresponding `.json` file sharing the same file ID.
---
## Annotation Format (Example)
```json
{
"image": {
"image_id": 979,
"width": 1500,
"height": 2060,
"file_name": "979.jpg"
},
"question": "how many windows on the building are not partially occluded by the balusters?",
"answer": "there are 13 windows that not partially occluded by the balusters.",
"task_type": "counting",
"task_domain": "outdoor",
"num_instances": 13,
"hallucination": 0,
"annotations": [
{
"id": 523353741,
"segmentation": {
"size": [
2060,
1500
],
"counts": "l][T1n0g4TOPe1e6I6M3N10000O2O0000000000000O100001O00000000000000000000000000000000000000000000000001O0000000000O1000000001O0000O2O00000000000001O0O1000000000O101O0001O00O1000O1001N10000O1O100O1O1O1O1O1N2O1O1O1_N^VNXLbi1f3cVNVL^i1i3gVNRLZi1m3SWNfKoh1X4TWNfKlh15ZVNV3k0dLmh1OdVNV3`0kLmh1JoVNR35SMQi1BZWNo2F^Mjj14VTN]1Q1^Nmj1OZTNHKV1P1SOoj1IjTNl0;ZO\\l1:hSNE`n1O2Lcejb1"
},
"bbox": [
578.0,
636.0,
112.0,
228.0
],
"area": 23791
}
]
}
```
Note: For brevity, only one of the 13 masks is shown.
### Field Descriptions
- **image**: Filename of the associated image and its metadata (e.g., width, height)
- **question**: Visual question answering (VQA)
- **answer**: Ground-truth answer (free-form)
- **task_type**: Type of reasoning (attribute, identification, OCR, counting, spatial, comparison)
- **task_domain**: Domain category (AV, indoor, outdoor, robotics, math, science)
- **num_instances**: Number of instances for visual evidence masks
- **hallucination**: Indicates whether valid visual evidence exists (0 = evidence present, 1 = no valid evidence)
- **annotations**: Segmentation mask(s) representing supporting evidence
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