Auto-Encoding Morph-Tokens for Multimodal LLM
<|reference_start|>Auto-Encoding Morph-Tokens for Multimodal LLM: 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.<|reference_end|>
@article{pan2024auto-encoding,
    title={Auto-Encoding Morph-Tokens for Multimodal LLM},
    author={Kaihang Pan, Siliang Tang, Juncheng Li, Zhaoyu Fan, Wei Chow,
  Shuicheng Yan, Tat-Seng Chua, Yueting Zhuang, Hanwang Zhang},
    journal={arXiv preprint arXiv:2405.01926},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01926},
    primaryClass={cs.CV}
}