Summary of Alt-moe:a Scalable Framework For Bidirectional Multimodal Alignment and Efficient Knowledge Integration, by Hongyang Lei et al.
Alt-MoE:A Scalable Framework for Bidirectional Multimodal Alignment and Efficient Knowledge Integration
by Hongyang Lei, Xiaolong Cheng, Dan Wang, Kun Fan, Qi Qin, Huazhen Huang, Yetao Wu, Qingqing Gu, Zhonglin Jiang, Yong Chen, Luo Ji
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A multimodal learning framework called Alt-MoE is introduced, which employs a mixture of experts (MoE) model as a multi-directional connector across modalities. The framework utilizes a sequential alternating one-way alignment strategy to iteratively refine the model, achieving bidirectional alignment in latent space. This enables efficient vector pre-storage and real-time retrieval via MoE, optimizing large-scale data processing. Alt-MoE achieves competitive performance on cross-modal retrieval and visual question answering by integrating diverse modality-specific knowledge, generalizing to unseen data, and easily scaling to new tasks and modalities through dynamic adjustment of MoE capacity and expert activation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal learning has made great progress in recent years. The goal is to combine different types of information from various sources into a shared understanding. Currently, there are two main ways to do this: direct alignment using pre-trained models or single-directional alignment using special connectors between the different modalities. However, these methods have some limitations. Direct alignment doesn’t make the most use of the rich information within each modality, and single-directional alignment can only handle one-way relationships between modalities. This paper introduces a new framework called Alt-MoE that addresses these limitations by using a mixture of experts (MoE) model as a connector across modalities. The framework iteratively refines its understanding of the different modalities to achieve a shared understanding. This approach has been shown to be effective in tasks such as cross-modal retrieval and visual question answering. |
Keywords
» Artificial intelligence » Alignment » Latent space » Mixture of experts » Question answering
