Auto-Encoding Morph-Tokens for Multimodal LLM

• URL: http://arxiv.org/abs/2405.01926v1
• Date: Fri, 3 May 2024 08:43:06 GMT
• Title: Auto-Encoding Morph-Tokens for Multimodal LLM
• Authors: Kaihang Pan, Siliang Tang, Juncheng Li, Zhaoyu Fan, Wei Chow, Shuicheng Yan, Tat-Seng Chua, Yueting Zhuang, Hanwang Zhang,
• Abstract summary: We propose encoding images into morph-tokens to serve a dual purpose: for comprehension, they act as visual prompts instructing MLLM to generate texts. Experiments show that morph-tokens can achieve a new SOTA for multimodal comprehension and generation simultaneously.
• Score: 151.2618346912529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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