Neural Networks for Visual Question Answering Architectures and Challenges

Abstract

Visual Question Answering (VQA) is a multidisciplinary research field at the intersection of computer vision, natural language processing, and artificial intelligence. VQA systems aim to provide accurate and contextually appropriate answers to questions about visual content, such as images and videos. This paper reviews the foundational concepts, state-of-the-art methods, datasets, evaluation metrics, challenges, and future directions in VQA.