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Deep Cross-Modal Integration: FUSION – A New Approach for Multimodal Language Models

The world of Artificial Intelligence (AI) is in constant motion. Especially in the field of multimodal language models (MLLMs), which can process both text and visual information, there is rapid progress. A promising new approach is FUSION, a family of MLLMs characterized by a complete integration of image and language representations. This article highlights the core innovations of FUSION and its potential for a deeper cross-modal understanding.

Beyond Late-Stage Integration: A Paradigm Shift

Previous MLLMs mostly rely on so-called late-stage integration, where the different modalities are only merged during decoding in the language model. FUSION, on the other hand, takes a different path: The integration of image and text occurs dynamically and deeply throughout the entire processing process. This allows for a much more comprehensive understanding of the relationships between visual and textual information.

Text-Guided Unified Vision Encoding: Pixel-Precise Integration

A central component of FUSION is the "Text-Guided Unified Vision Encoding". Here, textual information is already taken into account during image encoding to achieve pixel-precise integration. This allows the model to interpret the visual data in the context of the given text and thus develop a more precise understanding of the scene.

Context-Aware Recursive Alignment Decoding: Fine-Tuned Semantic Integration

Furthermore, FUSION uses a "Context-Aware Recursive Alignment Decoding". This method recursively aggregates visual features conditioned on the textual context during decoding. The result is a fine-tuned semantic integration at the question level, which enables a detailed and context-related understanding.

Dual-Supervised Semantic Mapping Loss and Synthetic Datasets

To optimize feature mapping and minimize modality discrepancies, the "Dual-Supervised Semantic Mapping Loss" is used. In addition, a synthetic question-answer dataset has been developed, based on high-quality question-answer pairs, which optimizes the text-guided feature integration.

Promising Results and Scalability

FUSION has been trained in two sizes – 3 billion and 8 billion parameters – and shows impressive results. With only 630 vision tokens, FUSION 3B already surpasses established models like Cambrian-1 8B and Florence-VL 8B in most benchmarks. Even with a reduced number of vision tokens (300), FUSION-L still achieves 95% of its original performance.

Conclusion: A Step Towards Deeper Cross-Modal Understanding

FUSION represents a significant advance in the field of multimodal language models. The complete integration of image and language representations throughout the entire processing process enables a deeper cross-modal understanding and opens up new possibilities for AI applications. The promising results underline the potential of this approach and lay the foundation for future developments in this dynamic research field.

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