Principled Multimodal Representation Learning

TL;DR

This paper introduces PMRL, a novel multimodal learning framework that aligns multiple data modalities simultaneously without relying on fixed anchors, using a rank-1 Gram matrix insight and a softmax-based loss for stable, unified representations.

Contribution

PMRL offers a new stable, anchor-free approach for multimodal alignment based on singular value optimization and eigenvector regularization, advancing the field.

Findings

Outperforms baseline methods across diverse tasks

Achieves stable and unified multimodal representations

Demonstrates effectiveness without anchor dependency

Abstract

Multimodal representation learning seeks to create a unified representation space by integrating diverse data modalities to improve multimodal understanding. Traditional methods often depend on pairwise contrastive learning, which relies on a predefined anchor modality, restricting alignment across all modalities. Recent advances have investigated the simultaneous alignment of multiple modalities, yet several challenges remain, such as limitations imposed by fixed anchor points and instability arising from optimizing the product of singular values. To address the challenges, in this paper, we propose Principled Multimodal Representation Learning (PMRL), a novel framework that achieves simultaneous alignment of multiple modalities without anchor dependency in a more stable manner. Specifically, grounded in the theoretical insight that full alignment corresponds to a rank-1 Gram matrix, PMRL optimizes the dominant singular value of the representation matrix to align modalities along a shared leading direction. We propose a softmax-based loss function that treats singular values as logits to prioritize the largest singular value. Besides, instance-wise contrastive regularization on the leading eigenvectors maintains inter-instance separability and prevents representation collapse. Extensive experiments across diverse tasks demonstrate PMRL's superiority compared to baseline methods. Source code can be found in https://github.com/Xiaohao-Liu/PMRL.