Visual Question Answering

The field asks how machines can connect a question to relevant visual evidence, read text and diagrams, and combine perception with commonsense or specialized knowledge. Approaches range from vision-language encoders and multimodal large language models to region and crop grounding, OCR and scene graphs, retrieval-augmented agents, and fine-tuning or reinforcement learning. Benchmarks cover everyday images and compositional reasoning, knowledge-based and text-rich questions, and document, video, medical, scientific, and multi-view settings. A central challenge is that correct-sounding answers may lack reliable visual grounding: models still miss small details and spatial relations, struggle to combine evidence across views or long documents, and may fail to recognize when they should abstain.