Mutimodal Clustering Based on Diffusion-Multimodal VAE

Revolutionizing Multimodal Understanding with D-MVAE

This paper introduces the Diffusion-based Multimodal Variational Autoencoder (D-MVAE) to address the challenge of semantic alignment and high-fidelity generation in multimodal learning. D-MVAE effectively combines the latent representation capabilities of Multimodal VAE (M-VAE) with the superior generative quality of Denoising Diffusion Probabilistic Models (DDPM), offering a robust solution for cross-modal consistency and generation.

The Challenge: Incomplete Multimodal Alignment & Blurry Generation

In multimodal learning, achieving semantic alignment between modalities such as images and text and high-fidelity generation is still a great challenge. A core issue is incomplete latent alignment: models often fail to map semantically similar content from different modalities into consistent regions of the latent space. This misalignment fundamentally undermines coherent multimodal understanding. Additionally, M-VAEs frequently generate blurry or imprecise reconstructions - whether in images or text - that lack critical detail, which can significantly limit their practical applicability in tasks such as cross-modal retrieval. Compounding these problems, the lack of reliable training strategies makes the models sensitive to real-world noise and increases the likelihood of convergence to suboptimal solutions with poor generalization.

Our Solution: D-MVAE - Integrating VAE & Diffusion for Enhanced Fidelity

To address this issue, we propose the Diffusion-based Multimodal Variational Autoencoder (D-MVAE), which can integrate the latent representation capabilities of the Multimodal Variational Autoencoder (M-VAE) with the outstanding generative quality of Denoising Diffusion Probabilistic Model (DDPM). Our model employs a phased training strategy to gradually align latent representations and facilitate cross-modal generation, while further enhancing the output through adversarial and diffusion techniques.