Cross-Modal Alignment via Variational Copula Modelling

TL;DR

This paper introduces a copula-based framework for multimodal learning that models complex interactions among different data modalities, improving the alignment and fusion of their representations, especially for missing data scenarios.

Contribution

It proposes a novel copula-driven approach to model joint distributions of multiple modalities, capturing higher-order interactions beyond simple concatenation methods.

Findings

Outperforms existing methods on MIMIC datasets

Effectively models complex modality interactions

Generates accurate representations for missing modalities

Abstract

Various data modalities are common in real-world applications (e.g., electronic health records, medical images and clinical notes in healthcare). It is essential to develop multimodal learning methods to aggregate various information from multiple modalities. The main challenge is how to appropriately align and fuse the representations of different modalities into a joint distribution. Existing methods mainly rely on concatenation or the Kronecker product, oversimplifying the interaction structure between modalities and indicating a need to model more complex interactions. Additionally, the joint distribution of latent representations with higher-order interactions is underexplored. Copula is a powerful statistical structure for modelling the interactions among variables, as it naturally bridges the joint distribution and marginal distributions of multiple variables. We propose a novel copula-driven multimodal learning framework, which focuses on learning the joint distribution of various modalities to capture the complex interactions among them. The key idea is to interpret the copula model as a tool to align the marginal distributions of the modalities efficiently. By assuming a Gaussian mixture distribution for each modality and a copula model on the joint distribution, our model can generate accurate representations for missing modalities. Extensive experiments on public MIMIC datasets demonstrate the superior performance of our model over other competitors. The code is available at https://github.com/HKU-MedAI/CMCM.