Alt-MoE:A Scalable Framework for Bidirectional Multimodal Alignment and Efficient Knowledge Integration

• URL: http://arxiv.org/abs/2409.05929v3
• Date: Fri, 20 Dec 2024 06:37:16 GMT
• Title: Alt-MoE:A Scalable Framework for Bidirectional Multimodal Alignment and Efficient Knowledge Integration
• Authors: Hongyang Lei, Xiaolong Cheng, Dan Wang, Kun Fan, Qi Qin, Huazhen Huang, Yetao Wu, Qingqing Gu, Zhonglin Jiang, Yong Chen, Luo Ji,
• Abstract summary: Multimodal learning has advanced significantly by aligning different modalities within shared latent spaces.<n>Direct alignment struggles to fully leverage rich intra-modal knowledge, often requiring extensive training data to achieve cross-modal representation.<n>We introduce Alt-MoE, a scalable multimodal alignment framework that employs a mixture of experts (MoE) model as a multi-directional connector across modalities.
• Score: 6.928469290518152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal learning has advanced significantly by aligning different modalities within shared latent spaces, enabling tasks such as cross-modal understanding and generation. Current alignment strategies in multimodal learning primarily include direct alignment using pre-trained or unified encoders and single-directional alignment via modality-specific connectors. Direct alignment struggles to fully leverage rich intra-modal knowledge, often requiring extensive training data to achieve cross-modal representation. Meanwhile, single-directional alignment methods, despite leveraging pre-trained knowledge, restrict task adaptability and hinder the model's ability to capture bidirectional relationships, leading to incomplete knowledge fusion and underutilization of complementary modality-specific information. To address these limitations, we introduce Alt-MoE, a scalable multimodal alignment framework that employs a mixture of experts (MoE) model as a multi-directional connector across modalities. By utilizing a sequential alternating one-way alignment strategy, Alt-MoE iteratively refines the model to achieve bidirectional alignment. Alt-MoE operates in latent space, enabling efficient vector pre-storage and real-time retrieval via MoE, optimizing large-scale data processing. Extensive empirical studies demonstrate that 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.

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