Principled Multimodal Representation Learning

• URL: http://arxiv.org/abs/2507.17343v1
• Date: Wed, 23 Jul 2025 09:12:25 GMT
• Title: Principled Multimodal Representation Learning
• Authors: Xiaohao Liu, Xiaobo Xia, See-Kiong Ng, Tat-Seng Chua,
• Abstract summary: Multimodal representation learning seeks to create a unified representation space by integrating diverse data modalities.<n>Recent advances have investigated the simultaneous alignment of multiple modalities, yet several challenges remain.<n>We propose Principled Multimodal Representation Learning (PMRL), a novel framework that achieves simultaneous alignment of multiple modalities.
• Score: 70.60542106731813
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Continual Multimodal Contrastive Learning [70.60542106731813]
  Multimodal contrastive learning (MCL) advances in aligning different modalities and generating multimodal representations in a joint space.<n>However, a critical yet often overlooked challenge remains: multimodal data is rarely collected in a single process, and training from scratch is computationally expensive.<n>In this paper, we formulate CMCL through two specialized principles of stability and plasticity.<n>We theoretically derive a novel optimization-based method, which projects updated gradients from dual sides onto subspaces where any gradient is prevented from interfering with the previously learned knowledge.
  arXiv  Detail & Related papers  (2025-03-19T07:57:08Z)
• DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning [7.947217265041953]
  Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities.<n>We introduce DecAlign, a novel hierarchical cross-modal alignment framework designed to decouple multimodal representations into modality-unique (heterogeneous) and modality-common (homogeneous) features.<n>Our experiments on four widely used multimodal benchmarks demonstrate that DecAlign consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-14T21:47:48Z)
• Deep Reversible Consistency Learning for Cross-modal Retrieval [12.174193446177778]
  Cross-modal retrieval (CMR) typically involves learning common representations to directly measure similarities between multimodal samples.<n>Most existing CMR methods assume multimodal samples in pairs and employ joint training to learn common representations.<n>We propose a novel method termed Deep Reversible Consistency Learning (DRCL) for cross-modal retrieval.
  arXiv  Detail & Related papers  (2025-01-10T03:35:22Z)
• Asymmetric Reinforcing against Multi-modal Representation Bias [59.685072206359855]
  We propose an Asymmetric Reinforcing method against Multimodal representation bias (ARM)<n>Our ARM dynamically reinforces the weak modalities while maintaining the ability to represent dominant modalities through conditional mutual information.<n>We have significantly improved the performance of multimodal learning, making notable progress in mitigating imbalanced multimodal learning.
  arXiv  Detail & Related papers  (2025-01-02T13:00:06Z)
• Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference [20.761803725098005]
  Multimodal variational autoencoders (VAEs) aim to capture shared latent representations by integrating information from different data modalities. A significant challenge is accurately inferring representations from any subset of modalities without training an impractical number of inference networks for all possible modality combinations. We introduce multimodal iterative amortized inference, an iterative refinement mechanism within the multimodal VAE framework.
  arXiv  Detail & Related papers  (2024-10-15T08:49:38Z)
• Enhancing Multimodal Unified Representations for Cross Modal Generalization [52.16653133604068]
  We propose Training-free Optimization of Codebook (TOC) and Fine and Coarse cross-modal Information Disentangling (FCID)<n>These methods refine the unified discrete representations from pretraining and perform fine- and coarse-grained information disentanglement tailored to the specific characteristics of each modality.
  arXiv  Detail & Related papers  (2024-03-08T09:16:47Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning [53.68371566336254]
  We argue that the key to better performance lies in meaningful latent modality structures instead of perfect modality alignment. Specifically, we design 1) a deep feature separation loss for intra-modality regularization; 2) a Brownian-bridge loss for inter-modality regularization; and 3) a geometric consistency loss for both intra- and inter-modality regularization.
  arXiv  Detail & Related papers  (2023-03-10T14:38:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.