Visual question answering (VQA) is a multimodal AI task in which a system produces a natural-language answer to a free-form question posed about an image or scene. It requires jointly grounding linguistic semantics in visual content, combining object recognition, spatial reasoning, and language understanding. VQA is a benchmark capability for vision-language models and a building block for assistive and augmented-reality interfaces.
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- Contemporary VQA systems encode the image and question into a shared representation and decode an answer, increasingly via large vision-language transformers trained on image-text corpora. Evaluation uses datasets such as VQAv2 and GQA; persistent challenges include language priors (answering plausibly without truly attending to the image) and compositional reasoning over rare attribute combinations.