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

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 LLMs. 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.

• The paper presents MAGE, a novel framework that integrates visual encoders and language models through an Intelligent Alignment Network for robust multimodal learning.
• It introduces a dual-loss training strategy combining cross-entropy and mean squared error to optimize both vector alignment and semantic enhancement.
• Experimental results on benchmarks like MME, MMBench, and SEED demonstrate improved performance and flexible any-to-any multimodal outputs.

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

Introduction

Multimodal learning, which involves processing and generating information across different types of data such as images and text, poses significant challenges in achieving effective integration of visual encoders and LLMs. The core challenges include semantic gaps and dimensional mismatches between vision and text modalities, resulting in information loss during propagation. Researchers have sought solutions to these issues to enhance the capabilities of multimodal LLMs (MLLMs). The paper "MAGE: Multimodal Alignment and Generation Enhancement via Bridging Visual and Semantic Spaces" proposes a novel framework—MAGE—that aims to bridge semantic spaces of vision and text using an innovative alignment mechanism known as the Intelligent Alignment Network (IAN).

Figure 1: An example of an attention map from the Intelligent Alignment Network (IAN) demonstrating spatial understanding capabilities.

MAGE Framework

Alignment Mechanism

The architecture of MAGE introduces the Intelligent Alignment Network (IAN), which comprises two components: vector alignment and semantic enhancement.

• Vector Alignment Module: This module maps visual feature vectors to a dimensional space consistent with the LLM input, ensuring structural dimensional alignment and eliminating modality gaps.
• Semantic Enhancement Module: After alignment, this module injects high-level semantic information into the visual features, enhancing their expressive power in the semantic space of the LLM.

The paper employs a dual-loss training strategy combining cross-entropy with mean squared error to enhance the alignment effect. This approach simultaneously optimizes both efficiency and semantic alignment, addressing gaps between heterogeneous data.

Figure 2: Detailed architecture of MAGE, illustrating vector alignment and semantic enhancement capabilities of IAN.

Innovative Features

To increase MAGE’s ability for "Any-to-Any" transformation, the framework develops a fine-tuning dataset for multimodal tool-calling instructions, expanding the model’s output capability. This novel architecture supports multimodal outputs which include images, audio, video, and structured task planning.

Figure 3: Hierarchical Workflow demonstrating MAGE's capability in task planning and execution, generating complex outputs.

Experimental Results

The proposed architecture excels across multiple multimodal benchmarks including MME, MMBench, and SEED, demonstrating superior performance in image-text alignment and semantic understanding tasks. Experimental results confirm that MAGE achieves significant performance improvements, outperforming existing methods in resource efficiency and output flexibility.

Implications and Future Directions

Enhancements in Multimodal Applications

The integration of the IAN module elevates MAGE’s proficiency in cross-modal tasks, setting a precedent for further research in bridging the gap between visual and language modalities. The flexible tool invocation framework also highlights potential advancements in developing more adaptive multimodal systems.

Future Research

The study opens new avenues for exploration in enhancing MLLMs to efficiently handle complex tasks across varying modalities. Future work may focus on refining alignment mechanisms and exploring new datasets for training models that accurately capture the deep semantic relationships between visual and textual data.

Figure 4: Illustration of MAGE's expanded multimodal applications, emphasizing the integration of tools for complex task generation.

Conclusion

The development of MAGE marks a significant advancement in multimodal learning, offering sophisticated techniques to diminish semantic and dimensional disparities between visual encoders and LLMs. With the Intelligent Alignment Network at its core, MAGE sets a high standard for achieving effective semantic alignment, paving the way for future innovations in multimodal model architectures.