TIPPo Framework Overview

Paper context

Paper title: TIP and Polish: Text-Image-Prototype Guided Multi-Modal Generation via Commonality-Discrepancy Modeling and Refinement 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. Passages referencing this figure: 2 Proposed Method We propose TIPPo, a multimodal generation framework that captures inter-modal commonalities and intra-modal differences, with various training objectives, as shown in Fig. 1 . Fig. 1 : The overview of TIPPo framework 2.