Matryoshka Multimodal Models With Adaptive Visual Tokenization: Enhancing Efficiency And Flexibility In Multimodal Machine Learning

Multimodal machine learning is a cutting-edge research field that combines different data types to create more comprehensive and accurate models. The key problem in this field is the inefficiency and inflexibility of large multimodal models when dealing with high-resolution images and videos. To address this issue, researchers have introduced Matryoshka Multimodal Models, which represent visual content as nested sets of visual tokens and allow for dynamic adjustment of token numbers based on visual complexity.