Multimodal generative models have recently gained significant attention for their ability to learn representations across various modalities, enhancing joint and cross-generation coherence. However, most existing works use standard Gaussian or Laplacian distributions as priors, which may struggle to capture the diverse information inherent in multiple data types due to their unimodal and less informative nature. Energy-based models (EBMs), known for their expressiveness and flexibility across various tasks, have yet to be thoroughly explored in the context of multimodal generative models. In this paper, we propose a novel framework that integrates the multimodal latent generative model with the EBM. Both models can be trained jointly through a variational scheme. This approach results in a more expressive and informative prior, better-capturing of information across multiple modalities. Our experiments validate the proposed model, demonstrating its superior generation coherence.

本研究针对现有多模态生成模型在信息捕捉上的局限性，提出了一种将能量模型（EBM）与多模态潜在生成模型相结合的新框架。该框架通过变分方法联动训练，提高了先验模型的表现力和信息捕捉能力，实验结果证明了其在生成一致性上的优越性。

基于能量模型的多模态潜在生成模型学习