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Enhancing multimodal deep representation learning by fixed model reuse

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Abstract

As we all know, inconsistent distribution and representation of different modalities, such as image, text and audio, cause the “media gap”, which poses a great challenge to deal with such heterogeneous data. Currently, state-of-the-art multimodal approaches mainly focus on the data provided by target task, neglecting the extra information on different but related tasks. In this paper, we explore a multimodal representation learning architecture by leveraging embedding representation trained from extra information. Specifically speaking, the approach of fixed model reuse is integrated into our architecture, which can incorporate helpful information from existing models/features into a new model. Based on our proposed architecture, we study multilingual OCR and long-text-based image retrieval tasks. Multilingual OCR is a difficult task that deals with multiple languages on the same page. We take advantage of extra textual embedding layer in an existing text-generating model to improve the accuracy of multilingual OCR. As for the long-text-based image retrieval, a cross-modal task, intermediate visual embedding layer in an off-the-shelf image-captioning model is leveraged to enhance the retrieval ability. The experimental results validate the effectiveness of our proposed architecture on narrowing down the “media gap” and yield observable improvement in these two tasks. Our architecture outperform the state-of-the-art approaches by 4.2% improvements in terms of accuracy in multilingual OCR task and yields improvement from 9 to 6 with regard to the median rank of retrieval result in long-text-based image retrieval task.

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Notes

  1. https://github.com/yashk2810/Image-Captioning

  2. https://pypi.org/project/jieba/

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Acknowledgements

This work is supported by the National Social Science Foundation of China (Grant No: 15BGL048), Hubei Province Science and Technology Support Project (Grant No: 2015BAA072), Hubei Provincial Natural Science Foundation of China (Grant No: 2017CFA012), The Fundamental Research Funds for the Central Universities (WUT: 2017II39GX).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan Shi, China

    Zhongwei Xie, Lin Li, Xian Zhong, Yang He & Luo Zhong

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  1. Zhongwei Xie
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  2. Lin Li
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Correspondence to Zhongwei Xie.

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Xie, Z., Li, L., Zhong, X. et al. Enhancing multimodal deep representation learning by fixed model reuse. Multimed Tools Appl 78, 30769–30791 (2019). https://doi.org/10.1007/s11042-018-6556-6

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