The paper introduces the task of Visual Question Answering (VQA), which involves generating natural language answers to open-ended questions about images. VQA is designed to mimic real-world scenarios, such as assisting visually impaired individuals, and requires a detailed understanding of the image and complex reasoning. The authors provide a dataset containing approximately 250,000 images, 760,000 questions, and 10 million answers, along with baselines and methods for VQA. They analyze the types of questions and answers, and discuss the information content of questions and answers compared to image captions. The paper also explores the challenges and potential of VQA, highlighting its ability to push the boundaries of multi-modal AI tasks. The authors organize an annual challenge and workshop to facilitate progress in VQA research.The paper introduces the task of Visual Question Answering (VQA), which involves generating natural language answers to open-ended questions about images. VQA is designed to mimic real-world scenarios, such as assisting visually impaired individuals, and requires a detailed understanding of the image and complex reasoning. The authors provide a dataset containing approximately 250,000 images, 760,000 questions, and 10 million answers, along with baselines and methods for VQA. They analyze the types of questions and answers, and discuss the information content of questions and answers compared to image captions. The paper also explores the challenges and potential of VQA, highlighting its ability to push the boundaries of multi-modal AI tasks. The authors organize an annual challenge and workshop to facilitate progress in VQA research.