Visual Question Answering

Visual Question Answering (VQA) aims to enable computers to answer questions about images, requiring sophisticated integration of visual and linguistic understanding. Current research emphasizes improving model robustness and reliability, focusing on addressing issues like inconsistencies in responses, hallucinations, and the handling of unanswerable questions, often using large multimodal language models (MLLMs) like BLIP-2 and LLaVA. This field is crucial for advancing AI's ability to interact with the world in a more human-like way, with applications ranging from assistive technologies for visually impaired individuals to medical image analysis and automated data visualization evaluation.