How does content-adaptive tokenization affect the inference latency and accuracy of multimodal vision-language

Abstract

Abstract: Modern multimodal large language models (MLLMs) typically keep the language model fixed and train a visual projector that maps the pixels into a sequence of tokens in its embedding space, so that images can be presented in essentially the same form as text. However, the language model has been optimized to operate on discrete, semantically meaningful tokens, while prevailing visual projectors transform an image into a long stream of continuous and highly correlated embeddings. This causes the visual tokens to behave differently from the word-like units that LLMs are originally trained to under

Research Question

How does content-adaptive tokenization affect the inference latency and accuracy of multimodal vision-language models on high-resolution image datasets compared to fixed-patch baselines?

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