metadata
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)
{
"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