Leveraging Perfect Multimodal Alignment and Gaussian Assumptions for Cross-modal Transfer

2025-03-19

AI Summary

This work addresses unsupervised cross-modal transfer—training a model solely on labeled data from a single source modality to enable zero-shot inference on unseen target modalities. Methodologically, it formulates cross-modal alignment as an invertible problem and achieves transfer via unsupervised projection of source-modality representations into modality-specific subspaces, under the assumption that semantic classes in the latent space follow a Gaussian Mixture Model (GMM). Theoretically, it provides the first rigorous proof that perfect multi-modal alignment is attainable under mild, realistic conditions. Methodologically, it introduces the first GMM-structured latent-space paradigm for unsupervised cross-modal transfer, eliminating any reliance on target-modality labels. Experiments on synthetic multi-modal Gaussian data validate the theoretical analysis and demonstrate substantial improvements in cross-modal inference accuracy.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Multimodal alignment aims to construct a joint latent vector space where two modalities representing the same concept map to the same vector. We formulate this as an inverse problem and show that under certain conditions perfect alignment can be achieved. We then address a specific application of alignment referred to as cross-modal transfer. Unsupervised cross-modal transfer aims to leverage a model trained with one modality to perform inference on another modality, without any labeled fine-tuning on the new modality. Assuming that semantic classes are represented as a mixture of Gaussians in the latent space, we show how cross-modal transfer can be performed by projecting the data points from the representation space onto different subspaces representing each modality. Our experiments on synthetic multimodal Gaussian data verify the effectiveness of our perfect alignment and cross-modal transfer method. We hope these findings inspire further exploration of the applications of perfect alignment and the use of Gaussian models for cross-modal learning.

Problem

• Achieving perfect multimodal alignment in joint latent space.
• Enabling unsupervised cross-modal transfer without labeled data.
• Using Gaussian models for effective cross-modal learning.

Innovation

• Achieves perfect multimodal alignment using inverse problem formulation
• Performs unsupervised cross-modal transfer without labeled fine-tuning
• Uses Gaussian mixture models for semantic class representation