Visual Question

Visual Question Answering (VQA) aims to develop systems that can accurately answer natural language questions about the content of images or videos. Current research focuses on improving model robustness and accuracy, particularly for complex questions requiring spatial reasoning, multi-modal fusion (combining visual and textual information), and handling diverse question types, often employing large language models (LLMs) and vision transformers (ViTs) within various architectures. The field's significance lies in its potential for applications ranging from assisting visually impaired individuals to enhancing medical diagnosis and autonomous driving, driving advancements in multimodal learning and reasoning.