Journal of Applied Science and Engineering

Published by Tamkang University Press


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Chunlin YuanThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of Civil Engineering and Architecture, Zhengzhou University of Science and Technology, Zhengzhou, 450064, China



Received: January 3, 2024
Accepted: March 4, 2024
Publication Date: April 13, 2024

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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Conventional few-shot multimedia image recognition methods in education management ignore the important semantic information in the training samples, resulting in insufficient feature learning, which is difficult to solve the problem of large intra-class variation. In this paper, we propose a global feature learning method based on multi-level semantic fusion. Specifically, according to the characteristics of different semantic levels of training samples, we design and implement different semantic learning tasks at the sample level, class level and task level, respectively. The semantic learning task and few-shot image classification task are integrated into the same architecture through the multi-task learning framework, which fuses the multi-level semantic information of categories and the discriminative information between classes. Therefore, the model can learn the category features from multiple perspectives, better find the commonality between samples with large differences, enhance the representativeness of the features. Compared with the baseline method, the large accuracy improvement is obtained on three datasets.

Keywords: Multimedia image recognition; education management; few-shot learning; multi-level semantic fusion

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