Aikyam-Lab/gridvqa-dataset

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.

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.