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Research Progress of Zero-Shot Learning Beyond Computer Vision

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Book cover Algorithms and Architectures for Parallel Processing (ICA3PP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12453))

Abstract

Traditional machine learning techniques, including deep learning, most assume that the classes of testing samples belong to the subset of training samples. However, there are many scenarios that conflict with this assumption in the real world, that is, the classes of testing samples have never been seen in model training. To improve the generalization ability of the model in these cases, zero-shot learning (ZSL) was proposed, which can mine the mapping relationship between the features and the labels of the seen class samples and then transfer it to the prediction of unseen classes. Most of the existing ZSL algorithms or applications are concerned with computer vision problems. In fact, the above difficulties and the demand for ZSL also exist in other fields, but there is currently a lack of relevant research progress review. To make up for this gap, this paper reviews the latest research progress of ZSL beyond computer vision, introduces the general concepts of ZSL, classifies the mainstream models, and refines three issues worthy of study. This study is expected to provide ZSL-based solution guidance for researchers and engineers beyond the field of computer vision.

This work was supported by National Natural Science Foundation of China (61836005) and the Opening Project of Shanghai Trusted Industrial Control Platform (TICPSH202003008-ZC).

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Weipeng Cao, Cong Zhou, Yuhao Wu, Zhong Ming & Zhiwu Xu

  2. School of Automation, Hangzhou Dianzi University, Hangzhou, China

    Jiyong Zhang

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  1. Weipeng Cao
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  2. Cong Zhou
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  3. Yuhao Wu
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  4. Zhong Ming
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  5. Zhiwu Xu
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  6. Jiyong Zhang
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Correspondence to Zhiwu Xu .

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Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

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Cao, W., Zhou, C., Wu, Y., Ming, Z., Xu, Z., Zhang, J. (2020). Research Progress of Zero-Shot Learning Beyond Computer Vision. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_36

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