Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
This work addresses the problem of limited multimodal reasoning in AI by enabling more integrated vision-language understanding and generation, representing a novel method rather than an incremental improvement.
The paper tackles the inequitable treatment of vision and language in multimodal models by introducing LaVIT, a foundation model that unifies image and text processing under a single generative paradigm, achieving large-margin performance improvements on numerous vision-language tasks.
Recently, the remarkable advance of the Large Language Model (LLM) has inspired researchers to transfer its extraordinary reasoning capability to both vision and language data. However, the prevailing approaches primarily regard the visual input as a prompt and focus exclusively on optimizing the text generation process conditioned upon vision content by a frozen LLM. Such an inequitable treatment of vision and language heavily constrains the model's potential. In this paper, we break through this limitation by representing both vision and language in a unified form. Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read. The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image. Coped with this tokenizer, the presented foundation model called LaVIT can handle both image and text indiscriminately under the same generative learning paradigm. This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously. Extensive experiments further showcase that it outperforms the existing models by a large margin on massive vision-language tasks. Our code and models are available at https://github.com/jy0205/LaVIT.