Open Ended Visual Question Answering

Open-ended visual question answering (VQA) aims to enable computers to answer complex, free-form questions about images, going beyond simple object recognition. Current research focuses on improving model capabilities through advanced architectures like large multimodal language models (LLMs) and incorporating external knowledge bases for more robust reasoning, often employing techniques like prefix tuning or generate-then-select strategies. These advancements are significant because they push the boundaries of visual understanding and language processing, with potential applications in diverse fields such as medical diagnosis, information retrieval, and assistive technologies.