mlsanityData to 14 Oct 2026

VQA: Visual Question Answering

arXiv 1505.00468 ↗First posted 3 May 2015cs.CL cs.CVcited by 2,506

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Cited by 750 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. Our VQA demo is available on CloudCV (http://cloudcv.org/vqa).

Key facts. Cited by 2,506 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2015.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 332; 2022: 389; 2023: 482; 2024: 498; 2025: 485; 2026: 318.

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  11. 2021NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actionscited by 744
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