Principled Multimodal Representation Learning

2025-07-23

AI Summary

Multimodal representation learning faces challenges including anchor-modality dependency—leading to insufficient cross-modal alignment—and instability in singular value optimization. To address these, we propose an anchor-free multimodal joint alignment framework: (i) we design a softmax loss over dominant singular values as logits to enforce alignment along shared principal directions across modalities; and (ii) we introduce instance-level contrastive regularization to enhance class separability and training stability. Theoretical analysis grounded in singular value decomposition (SVD) characterizes structural properties of the learned representation matrices. Extensive experiments demonstrate significant improvements over state-of-the-art baselines on cross-modal retrieval and classification tasks, validating the method’s effectiveness, robustness, and generalization capability. The source code will be made publicly available.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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. The source code will be publicly available.

Problem

• Align multiple modalities without anchor dependency
• Overcome instability from singular value optimization
• Create unified multimodal representation space

Innovation

• Simultaneous alignment of multiple modalities without anchors
• Optimizes dominant singular value for shared direction alignment
• Softmax-based loss prioritizes largest singular value