Amplifying Prominent Representations in Multimodal Learning via Variational Dirichlet Process

TL;DR

This paper introduces a Bayesian non-parametric framework using the Dirichlet process to enhance multimodal learning by dynamically emphasizing prominent features within each modality, improving fusion and representation quality.

Contribution

The proposed DP-driven framework uniquely balances intra-modal feature prominence and cross-modal alignment, advancing multimodal fusion techniques with adaptive feature weighting.

Findings

Outperforms existing multimodal models on several datasets.

Demonstrates robustness to hyperparameter variations.

Validates effectiveness through ablation studies.

Abstract

Developing effective multimodal fusion approaches has become increasingly essential in many real-world scenarios, such as health care and finance. The key challenge is how to preserve the feature expressiveness in each modality while learning cross-modal interactions. Previous approaches primarily focus on the cross-modal alignment, while over-emphasis on the alignment of marginal distributions of modalities may impose excess regularization and obstruct meaningful representations within each modality. The Dirichlet process (DP) mixture model is a powerful Bayesian non-parametric method that can amplify the most prominent features by its richer-gets-richer property, which allocates increasing weights to them. Inspired by this unique characteristic of DP, we propose a new DP-driven multimodal learning framework that automatically achieves an optimal balance between prominent intra-modal representation learning and cross-modal alignment. Specifically, we assume that each modality follows a mixture of multivariate Gaussian distributions and further adopt DP to calculate the mixture weights for all the components. This paradigm allows DP to dynamically allocate the contributions of features and select the most prominent ones, leveraging its richer-gets-richer property, thus facilitating multimodal feature fusion. Extensive experiments on several multimodal datasets demonstrate the superior performance of our model over other competitors. Ablation analysis further validates the effectiveness of DP in aligning modality distributions and its robustness to changes in key hyperparameters. Code is anonymously available at https://github.com/HKU-MedAI/DPMM.git