Generalizing Multimodal Variational Methods to Sets

• URL: http://arxiv.org/abs/2212.09918v1
• Date: Mon, 19 Dec 2022 23:50:19 GMT
• Title: Generalizing Multimodal Variational Methods to Sets
• Authors: Jinzhao Zhou and Yiqun Duan and Zhihong Chen and Yu-Cheng Chang and Chin-Teng Lin
• Abstract summary: This paper presents a novel variational method on sets called the Set Multimodal VAE (SMVAE) for learning a multimodal latent space. By modeling the joint-modality posterior distribution directly, the proposed SMVAE learns to exchange information between multiple modalities and compensate for the drawbacks caused by factorization.
• Score: 35.69942798534849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Making sense of multiple modalities can yield a more comprehensive description of real-world phenomena. However, learning the co-representation of diverse modalities is still a long-standing endeavor in emerging machine learning applications and research. Previous generative approaches for multimodal input approximate a joint-modality posterior by uni-modality posteriors as product-of-experts (PoE) or mixture-of-experts (MoE). We argue that these approximations lead to a defective bound for the optimization process and loss of semantic connection among modalities. This paper presents a novel variational method on sets called the Set Multimodal VAE (SMVAE) for learning a multimodal latent space while handling the missing modality problem. By modeling the joint-modality posterior distribution directly, the proposed SMVAE learns to exchange information between multiple modalities and compensate for the drawbacks caused by factorization. In public datasets of various domains, the experimental results demonstrate that the proposed method is applicable to order-agnostic cross-modal generation while achieving outstanding performance compared to the state-of-the-art multimodal methods. The source code for our method is available online https://anonymous.4open.science/r/SMVAE-9B3C/.

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