Vision Language Adapter

Vision-language adapters aim to effectively integrate visual and textual information within large language models (LLMs), enhancing their ability to perform multimodal tasks like visual question answering and image captioning. Current research focuses on improving the accuracy and calibration of these adapters, exploring architectures like the perceiver resampler and developing training strategies that address issues such as slow convergence and miscalibration, particularly in out-of-distribution scenarios. This work is significant because it enables LLMs to understand and reason with visual data, leading to advancements in various applications requiring multimodal understanding, such as improved search engines and more sophisticated AI assistants.