What is it about?

Can computers understand images and answer questions about them, like people do? This paper introduces Visual Question Answering (VQA), a technology that allows artificial intelligence to look at pictures and answer natural language questions about them. We explain how this works, what data is used to train such systems, and what challenges researchers still face. This summary gives readers a simple overview of how AI is learning to see and respond—just like a human.

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Why is it important?

As AI continues to rapidly advance, helping machines understand images and respond to questions is more important than ever. While previous reviews on Visual Question Answering (VQA) focused only on certain technical approaches, this paper offers the first comprehensive overview that brings together all major techniques, datasets, and evaluation methods in one place. It helps researchers, developers, and educators get a clear and unified view of the field—and paves the way for smarter, more explainable AI systems that interact with the visual world.

Perspectives

Working on this survey gave me the chance to step back and look at how far the VQA field has come. It reminded me why I got interested in AI in the first place—helping machines better understand the world around them. I also really enjoyed working with co-authors who brought unique perspectives to the paper. I hope this work not only informs but inspires others to keep building smarter, more responsible AI systems.

Byeong Su Kim
Yonsei University

Read the Original

This page is a summary of: Visual Question Answering: A Survey of Methods, Datasets, Evaluation, and Challenges, ACM Computing Surveys, April 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3728635.
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