M$^{2}$Chat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation

• URL: http://arxiv.org/abs/2311.17963v2
• Date: Sat, 13 Apr 2024 04:16:18 GMT
• Title: M$^{2}$Chat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation
• Authors: Xiaowei Chi, Rongyu Zhang, Zhengkai Jiang, Yijiang Liu, Yatian Wang, Xingqun Qi, Wenhan Luo, Peng Gao, Shanghang Zhang, Qifeng Liu, Yike Guo,
• Abstract summary: We propose textbf$M2Chat$, a novel unified multimodal LLM framework for generating interleaved text-image conversation. $M3Adapter$ integrates granular low-level visual information and high-level semantic features from multi-modality prompts. $M3FT$ fine-tuning strategy optimize disjoint groups of parameters for image-text alignment and visual-instruction.
• Score: 45.79215260916687
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While current LLM chatbots like GPT-4V bridge the gap between human instructions and visual representations to enable text-image generations, they still lack efficient alignment methods for high-fidelity performance on multiple downstream tasks. In this paper, we propose \textbf{$M^{2}Chat$}, a novel unified multimodal LLM framework for generating interleaved text-image conversation across various scenarios. Specifically, we propose an $M^{3}Adapter$ that efficiently integrates granular low-level visual information and high-level semantic features from multi-modality prompts. Upon the well-aligned fused feature, $M^{3}Adapter$ tailors a learnable gating strategy to balance the model creativity and consistency across various tasks adaptively. Moreover, to further enhance the effectiveness of $M^{3}Adapter$ while preserving the coherence of semantic context comprehension, we introduce a two-stage $M^{3}FT$ fine-tuning strategy. This strategy optimizes disjoint groups of parameters for image-text alignment and visual-instruction respectively. Extensive experiments demonstrate our $M^{2}Chat$ surpasses state-of-the-art counterparts across diverse benchmarks, showcasing its prowess in interleaving generation, storytelling, and multimodal dialogue systems. The demo and code are available at \red{https://mattie-e.github.io/M2Chat.github.io}.

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