GQA (General Question Answering) is a dataset for compositional visual reasoning — focusing on real-world images but using procedurally generated questions to rigorously test spatial understanding, object attributes, and multi-hop logic without the ambiguity of free-form text.

What Is GQA?

• Definition: A scene-graph-based VQA dataset.
• Construction: Images are annotated with dense scene graphs (objects, attributes, relations). Questions are generated from these graphs.
• Metric: Measures consistency and grounding, not just accuracy.
• Scale: 22M questions over 113K images.

Why GQA Matters

• Compositionality: Tests if the model understands "The red car to the left of the tree" vs "The tree to the left of the red car".
• Fine-Grained Analysis: Breaks down performance by skill (spatial, logical, comparative).
• Diagnostic: Helps researchers debug why a model fails (e.g., "it knows colors but fails at spatial relations").

GQA is a rigorous audit of visual syntax — ensuring models actually understand the structure of the visual world rather than just recognizing keywords.