Robust network based on embedded convolutional block attention module and earth mover’s distance for few-shot learning

Tong Zhang; Min Xin; Yinan Zhang; Jiashe Liao; Qingfeng Xie

doi:10.1117/12.3005795

10 October 2023 Robust network based on embedded convolutional block attention module and earth mover’s distance for few-shot learning

Tong Zhang, Min Xin, Yinan Zhang, Jiashe Liao, Qingfeng Xie

Author Affiliations +

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127992D (2023) https://doi.org/10.1117/12.3005795
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Deep neural networks of training require a large quantity of effectively labeled datasets, and models trained with insufficient samples will cause a variety of problems, such as overfitting and poor generalization ability. Few-shot learning has been proposed to settle the problem, and the methods introduced in some papers have achieved impressive results. However, most of the methods have poor robustness due to the under-utilization of the global information and local information of the image. For solving the above problems, an improved classification network is proposed in this paper, which introduces two modifications. On the one hand, Convolutional Block Attention Module (CBAM) is embedded in the network for extracting global features, thus the model can quickly suit various tasks. On the other hand, the image is randomly divided into tiles and represented as a set of local features, the Earth Mover’s Distance (EMD) is employed to calculate the distance between embedded sets of two images. Finally, the weighted sum of these two parts’ classification errors, is computed as the total classification error of the proposed model. Experiments are conducted on two benchmark datasets: mini-ImageNet and tiered-ImageNet, and the results demonstrate that our proposed method achieves state-of-theart(SOTA) results in the field of few-shot image classification.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Tong Zhang, Min Xin, Yinan Zhang, Jiashe Liao, and Qingfeng Xie "Robust network based on embedded convolutional block attention module and earth mover’s distance for few-shot learning", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127992D (10 October 2023); https://doi.org/10.1117/12.3005795

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KEYWORDS

Image classification

Image processing

Data modeling

Feature extraction

Neural networks

Ablation

Image retrieval

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