MAGE: Multimodal Alignment and Generation Enhancement via Bridging Visual and Semantic Spaces

2025-07-29

AI Summary

To address the dual loss of spatial structure and semantic information after visual encoding in multimodal models—leading to insufficient coupling between visual encoders and large language models—this paper proposes the Intelligent Alignment Network (IAN). IAN employs learnable cross-modal mappings to achieve simultaneous alignment in both dimensionality and semantics. We further design a multi-task loss function that jointly optimizes cross-entropy and mean squared error, mitigating representational discrepancies among semantically equivalent but structurally heterogeneous data. Additionally, we construct a multimodal tool-use instruction-tuning dataset to enhance Any-to-Any generation capability. Extensive experiments on benchmarks including MME, MMBench, and SEED demonstrate significant improvements over state-of-the-art methods, validating the efficacy of our cross-modal alignment strategy. The code and supplementary materials are publicly available.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

In the latest advancements in multimodal learning, effectively addressing the spatial and semantic losses of visual data after encoding remains a critical challenge. This is because the performance of large multimodal models is positively correlated with the coupling between visual encoders and large language models. Existing approaches often face issues such as vector gaps or semantic disparities, resulting in information loss during the propagation process. To address these issues, we propose MAGE (Multimodal Alignment and Generation Enhancement), a novel framework that bridges the semantic spaces of vision and text through an innovative alignment mechanism. By introducing the Intelligent Alignment Network (IAN), MAGE achieves dimensional and semantic alignment. To reduce the gap between synonymous heterogeneous data, we employ a training strategy that combines cross-entropy and mean squared error, significantly enhancing the alignment effect. Moreover, to enhance MAGE's "Any-to-Any" capability, we developed a fine-tuning dataset for multimodal tool-calling instructions to expand the model's output capability boundaries. Finally, our proposed multimodal large model architecture, MAGE, achieved significantly better performance compared to similar works across various evaluation benchmarks, including MME, MMBench, and SEED. Complete code and appendix are available at: https://github.com/GTCOM-NLP/MAGE.

Problem

• Addressing spatial and semantic losses in visual data encoding
• Bridging semantic gaps between vision and text modalities
• Enhancing multimodal model performance via alignment and generation

Innovation

• Bridges visual and text semantic spaces
• Uses Intelligent Alignment Network (IAN)
• Combines cross-entropy and MSE training