Datasets:

Aikyam-Lab
/

gridvqa-dataset

language:
  - en
license: mit
task_categories:
  - image-text-to-text
  - visual-question-answering
tags:
  - vision-language
  - vqa
  - diagnostic-benchmark
  - xai

GridVQA-X Datasets

GridVQA-X is the first diagnostic framework designed to objectively evaluate the faithfulness of post-hoc cross-modal explainers. By utilizing a closed-world synthesis logic with mathematically guaranteed unique ground-truth explanations, it provides a controlled testbed to isolate genuine cross-modal spatial reasoning from shallow shortcuts.

Paper: GridVQA-X: A Framework for Evaluating Multimodal Explainability Methods
Repository: GitHub - AikyamLab/grid-vqax

Dataset Summary

The dataset features S × S visual grids populated by geometric objects, paired with multi-hop spatial reasoning queries. Each query defines a Target object to find/count and one or more Anchor reference objects connected by directional tokens.

The dataset contains two distinct, parallel splits:

D_pure (The Causal Split): Systematically removes all statistical heuristics. It algorithmically populates invalid spatial regions with adversarial distractors matching target attributes, forcing models to utilize complete multi-hop spatial geometry.
D_spur (The Shortcut Split): Intentionally preserves the pervasive "Bag-of-Words Alignment" cross-modal shortcut. No target-attribute objects are placed outside the valid spatial region, creating a behavioral trap where the probability of the answer given target attributes is 1.0.

Dataset Taxonomy and Configurations

Every sample is parameterized by a strict 4-tuple configuration: (Depth, QType, Form, Density)

Spatial Depth (1, 2, or 3): Controls the number of anchor objects and relational multi-hop complexity.
Question Type (QType):
- Attribute-Only (A): Non-spatial baseline queries.
- Shape-Only (SO) / Color-Only (CO): Isolates directional reasoning to a single feature type.
- Mixed (M): Requires multimodal binding of shape and color for both target and anchor.
- Comparison (CMP): Multi-step logical operations comparing object counts.
Form (0 or 1): Alters the target task header between counting (0) and existence (1) to prevent statistical format overfitting.
Density (d_0.3 or d_0.7): Adjusts grid object count to test spatial localization fidelity against background noise.

Ground-Truth Explanations

The dataset provides a mathematically proven, unique ground-truth causal explanation. The ground-truth visual masks are strictly bounded to the target and anchor items (A ∪ T), leaving all other distractor objects with a causal effect of exactly zero.

Citation

If you use GridVQA-X in your research, please cite:

@misc{belsare2026gridvqaxframeworkevaluatingmultimodal,
      title={GridVQA-X: A Framework for Evaluating Multimodal Explainability Methods}, 
      author={Sujay Belsare and Sudarshan Nikhil and Sushant Kumar and Ponnurangam Kumaraguru and Chirag Agarwal},
      year={2026},
      eprint={2606.14740},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.14740}, 
}