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arXiv · 2405.01926

Auto-Encoding Morph-Tokens for Multimodal LLM

Kaihang Pan·Siliang Tang·Juncheng Li·Zhaoyu Fan·Wei Chow·Shuicheng Yan·Tat-Seng Chua·Yueting Zhuang·Hanwang Zhang
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Abstract

For multimodal LLMs, the synergy of visual comprehension (textual output) and generation (visual output) presents an ongoing challenge. This is due to a conflicting objective: for comprehension, an MLLM needs to abstract the visuals; for generation, it needs to preserve the visuals as much as possible. Thus, the objective is a dilemma for visual-tokens. To resolve the conflict, we propose encoding images into morph-tokens to serve a dual purpose: for comprehension, they act as visual prompts instructing MLLM to generate texts; for generation, they take on a different, non-conflicting role as complete visual-tokens for image reconstruction, where the missing visual cues are recovered by the MLLM. Extensive experiments show that morph-tokens can achieve a new SOTA for multimodal comprehension and generation simultaneously. Our project is available at https://github.com/DCDmllm/MorphTokens.

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Kaihang Pan, Siliang Tang, Juncheng Li, Zhaoyu Fan, Wei Chow, Shuicheng Yan, Tat-Seng Chua, Yueting Zhuang, Hanwang Zhang. 2024-05-03. Auto-Encoding Morph-Tokens for Multimodal LLM. https://arxiv.org/abs/2405.01926

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