VQA
Shorthand for visual question answering - the ability to answer questions posed in natural language about visual content.
Think of it like
Like the parlor game "I spy" - someone describes what they see, you guess what it is. VQA is the reverse: you see the image, you answer the description.
Example
A VQA system shown a photo with people, trees, and a bench answers "Are there more people or trees?" or "What is the bench made of?"
How it actually works
VQA models encode images into visual feature maps, encode questions into embeddings, fuse them (often with attention), and decode an answer. Quality degrades when images are cluttered, questions are compositional (multi-step reasoning), or the model has never seen similar image-question pairs. Large vision-language models have reduced but not eliminated this.
For product teams
Widely used in e-commerce (product questions), accessibility, and interactive AI interfaces.
For engineers
Standard benchmark datasets: VQA v2, GQA. Metrics: accuracy (exact match), BLEU, CIDEr.
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