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An Optimization Algorithm for Multimodal Data Alignment

arXiv 2503.07636 ↗First posted 5 Mar 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 54 references that are in the map. First posted on arXiv 5 Mar 2025.

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References · 30

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