gqa (general question answering)

**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.