What is Visual Question Answering (VQA)?

Visual Question Answering (VQA) is an advanced field in artificial intelligence focusing on the capability of computers to analyze images and respond to related questions in a human-like manner by integrating computer vision and natural language processing (NLP). This article introduces the concept of VQA and explores various approaches, including the Bayesian model, Attention-based mechanisms, and models like Pix2Struct, BLIP-2, and GPT-4 with Vision. These models employ different strategies to combine visual and textual data, leveraging techniques such as feature extraction through CNNs and transformers to generate answers. The evaluation of VQA models often extends beyond traditional accuracy metrics, using measures like WUPS and METEOR to assess the semantic quality of responses. The article also discusses datasets like COCO-QA and DAQUAR that support the training and evaluation of VQA systems. Overall, VQA presents significant potential in enhancing how machines interpret and interact with visual information.