Visual Question Answering (VQA) is a task where a system is given an image and a natural language question about the image, and it must provide an accurate natural language answer. The questions and answers are open-ended, similar to real-world scenarios such as helping the visually impaired. VQA requires a detailed understanding of the image and complex reasoning, and it is amenable to automatic evaluation. A dataset containing approximately 250,000 images, 760,000 questions, and 10 million answers is provided. The dataset includes images from the MS COCO dataset and a newly created abstract scene dataset. The dataset is used to evaluate various baselines and methods for VQA. The paper also discusses the challenges of VQA, including the need for multi-modal knowledge, and the importance of quantitative evaluation. The paper introduces a VQA challenge and workshop to promote progress in this area. The results show that the best model achieves 58.16% accuracy on the open-ended task and 63.09% on the multiple-choice task. The paper also discusses the importance of commonsense knowledge and the role of image understanding in answering questions. The results indicate that the best model performs well on common visual objects but struggles with counts and higher counts. The paper also analyzes the performance of the model on questions that require different levels of commonsense knowledge and age-related understanding. The results show that the model performs well on questions that are answerable by children and that it has a moderate level of commonsense knowledge. The paper concludes that VQA is a challenging task that requires a combination of computer vision, natural language processing, and knowledge representation. The paper also suggests that future research should focus on improving the model's ability to handle complex reasoning and commonsense knowledge.Visual Question Answering (VQA) is a task where a system is given an image and a natural language question about the image, and it must provide an accurate natural language answer. The questions and answers are open-ended, similar to real-world scenarios such as helping the visually impaired. VQA requires a detailed understanding of the image and complex reasoning, and it is amenable to automatic evaluation. A dataset containing approximately 250,000 images, 760,000 questions, and 10 million answers is provided. The dataset includes images from the MS COCO dataset and a newly created abstract scene dataset. The dataset is used to evaluate various baselines and methods for VQA. The paper also discusses the challenges of VQA, including the need for multi-modal knowledge, and the importance of quantitative evaluation. The paper introduces a VQA challenge and workshop to promote progress in this area. The results show that the best model achieves 58.16% accuracy on the open-ended task and 63.09% on the multiple-choice task. The paper also discusses the importance of commonsense knowledge and the role of image understanding in answering questions. The results indicate that the best model performs well on common visual objects but struggles with counts and higher counts. The paper also analyzes the performance of the model on questions that require different levels of commonsense knowledge and age-related understanding. The results show that the model performs well on questions that are answerable by children and that it has a moderate level of commonsense knowledge. The paper concludes that VQA is a challenging task that requires a combination of computer vision, natural language processing, and knowledge representation. The paper also suggests that future research should focus on improving the model's ability to handle complex reasoning and commonsense knowledge.