A deep multimodal generative and fusion framework for class-imbalanced multimodal data

Abstract

The purpose of multimodal classification is to integrate features from diverse information sources to make decisions. The interactions between different modalities are crucial to this task. However, common strategies in previous studies have been to either concatenate features from various sources into a single compound vector or input them separately into several different classifiers that are then assembled into a single robust classifier to generate the final prediction. Both of these approaches weaken or even ignore the interactions among different feature modalities. In addition, in the case of class-imbalanced data, multimodal classification becomes troublesome. In this study, we propose a deep multimodal generative and fusion framework for multimodal classification with class-imbalanced data. This framework consists of two modules: a deep multimodal generative adversarial network (DMGAN) and a deep multimodal hybrid fusion network (DMHFN). The DMGAN is used to handle the class imbalance problem. The DMHFN identifies fine-grained interactions and integrates different information sources for multimodal classification. Experiments on a faculty homepage dataset show the superiority of our framework compared to several start-of-the-art methods.

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Notes

  1. 1.

    https://github.com/kennis-coder/multimodal_generative_fusion_framework.git

  2. 2.

    This model comprises 3 million 300-dimensional English word vectors and is accessible at https://code.google.com/archive/p/word2vec/

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (71671141 and 71873108), the National Social Science Foundation of China (NSSFC) (Grant No. 19BFX120), the Fundamental Research Funds for the Central Universities (JBK 171113, JBK 170505, JBK 1806003, and JBK 2002030), the Science and Technology Department of Sichuan Province (2019YJ0250), the Fintech Innovation Center of Southwestern University of Finance and Economics, and the Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province.

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Correspondence to Guanyuan Yu.

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Li, Q., Yu, G., Wang, J. et al. A deep multimodal generative and fusion framework for class-imbalanced multimodal data. Multimed Tools Appl (2020). https://doi.org/10.1007/s11042-020-09227-4

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Keywords

  • Multimodal classification
  • Class-imbalanced data
  • Deep multimodal generative adversarial network
  • Deep multimodal hybrid fusion network