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AI Research
arXiv CS.AI·Jun 9·5 min read
MM-Matryoshka: Towards Budget-Elastic Visual Document Retrieval via a 2D Multimodal Matryoshka Training Framework
TL;DR
Existing visual document retrieval systems struggle with high storage and computational costs due to their reliance on multiple vectors from Vision-Language Models (VLMs). MM-Matryoshka is a new 2D Matryoshka training framework that allows for budget-elastic retrieval by optimizing both vector width and encoder depth.
✦ Why It Matters
Engineers can implement MM-Matryoshka to optimize visual document retrieval systems for better resource management.
Key Takeaways
- Explore MM-Matryoshka for efficient document retrieval.
- Balance vector width and encoder depth in models.
- Reduce computational costs by up to 30%.
- Implement adaptive training for multimodal systems.
Full Summary
Visual document retrieval systems utilize multiple vectors from Vision-Language Models (VLMs) to achieve precise matching, but this leads to significant storage and computational demands. MM-Matryoshka introduces a 2D Matryoshka training framework that addresses this issue by allowing for a flexible balance between the number of vectors (vector width) and the complexity of the model (encoder depth).
The methodology involves training the model to adaptively adjust these parameters based on available resources while maintaining retrieval accuracy. Results indicate that MM-Matryoshka can reduce computational overhead by up to 30% without sacrificing performance.
This framework not only enhances efficiency but also provides a unified approach for optimizing multimodal retrieval systems. Engineers and researchers can leverage this framework to develop more scalable and cost-effective visual document retrieval solutions.