TIP and Polish: Text-Image-Prototype Guided Multi-Modal Generation via Commonality-Discrepancy Modeling and Refinement

• URL: http://arxiv.org/abs/2511.21698v1
• Date: Wed, 12 Nov 2025 07:16:19 GMT
• Title: TIP and Polish: Text-Image-Prototype Guided Multi-Modal Generation via Commonality-Discrepancy Modeling and Refinement
• Abstract summary: Existing methods suffer from cross-modal mismatch and lack explicit modeling of commonality and discrepancy.<n>We propose textbftextitTIPPo, a framework with explicit input modeling and comprehensive optimization objectives.<n> Experimental results demonstrate the promising performance of textbftextitTIPPo in automatic evaluation and LLM-based criteria for creativity and semantic consistency.
• Score: 6.795398125733378
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-modal generation struggles to ensure thematic coherence and style consistency. Semantically, existing methods suffer from cross-modal mismatch and lack explicit modeling of commonality and discrepancy. Methods that rely on fine-grained training fail to balance semantic precision with writing style consistency. These shortcomings lead to suboptimal generation quality. To tackle these issues, we propose \textbf{\textit{TIPPo}}, a simple yet effective framework with explicit input modeling and comprehensive optimization objectives. It extracts the input text and images via multi-modal encoder and adapters, then measures the visual prototype. \textbf{T}extual, \textbf{I}mage, and \textbf{P}rototype signals are then fed to our proposed Dual Alignment Attention and Difference Operator modules before language model decoding. The proposed \textbf{Po}lishPPO reinforces the style consistency, while the unsupervised contrastive learning during SFT mitigates inter-sample representation collapse. Experimental results demonstrate the promising performance of \textbf{\textit{TIPPo}} in automatic evaluation and LLM-based criteria for creativity and semantic consistency.

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