提出新算法提升多模态数据对齐效果
An Optimization Algorithm for Multimodal Data Alignment
- 基于核CCA思想设计优化算法，统一多模态数据表示
- 在检索与分类任务中显著提升多模态数据表征能力
- 适合需要跨模态融合的模型开发人员参考
在数据时代，多模态数据融合已成为研究热点，旨在构建可跨多种模态和领域灵活使用的先进多模态模型。尽管投入大量研发，如何在单一统一潜在空间中最优表示不同形式的数据——这一实现有效多模态推理的关键步骤——仍未得到充分解决。为此，本文提出AlignXpert，一种受核典型相关分析（Kernel CCA）启发的优化算法，旨在最大化N个模态之间的相似性，同时施加其他约束。实验表明，该方法显著提升了多种推理任务（如检索与分类）中的数据表示质量，凸显了数据表示在多模态建模中的核心作用。
原文摘要 · Abstract (English)
In the data era, the integration of multiple data types, known as multimodality, has become a key area of interest in the research community. This interest is driven by the goal to develop cutting edge multimodal models capable of serving as adaptable reasoning engines across a wide range of modalities and domains. Despite the fervent development efforts, the challenge of optimally representing different forms of data within a single unified latent space a crucial step for enabling effective multimodal reasoning has not been fully addressed. To bridge this gap, we introduce AlignXpert, an optimization algorithm inspired by Kernel CCA crafted to maximize the similarities between N modalities while imposing some other constraints. This work demonstrates the impact on improving data representation for a variety of reasoning tasks, such as retrieval and classification, underlining the pivotal importance of data representation.
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