Knowledge Based Visual Question Answering

Knowledge-based visual question answering (KB-VQA) aims to enable computers to answer questions about images by leveraging external knowledge sources beyond the image itself. Current research focuses on improving the efficiency and accuracy of multimodal models, often employing retrieval-augmented generation (RAG) frameworks and large language models (LLMs) to integrate visual and textual information with external knowledge bases. These advancements address limitations in existing methods, such as inefficient inference and the need for more effective knowledge retrieval and fusion techniques. The resulting improvements in KB-VQA have significant implications for applications requiring complex visual reasoning and knowledge integration, such as robotics, education, and information retrieval.