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Related Experiment Video

Updated: Aug 12, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Cross-domain few-shot learning based on pseudo-Siamese neural network.

Yuxuan Gong¹, Yuqi Yue¹, Weidong Ji²

¹School of Computer Science and Information Engineering, Harbin Normal University, Harbin, 150025, Heilongjiang, People's Republic of China.

Scientific Reports

|January 25, 2023

Summary

This study introduces a novel pseudo-Siamese neural network for cross-domain few-shot learning, improving accuracy by using contour features from sketch maps alongside original images. This approach enhances model generalization across different datasets.

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Area of Science:

Machine Learning
Computer Vision

Background:

Cross-domain few-shot learning faces accuracy challenges due to domain discrepancies.
Existing methods struggle to generalize effectively across diverse datasets.

Purpose of the Study:

To propose a novel few-shot learning method to address the accuracy drop in cross-domain learning.
To enhance model generalization by integrating contour features.

Main Methods:

A pseudo-Siamese convolutional neural network architecture was developed.
Original images and sketch maps were processed through separate network branches.
Contour features were extracted and utilized alongside original image features during training.

Main Results:

Experiments demonstrated improved accuracy and generalization in cross-domain few-shot learning tasks.
The method achieved good results using mini-ImageNet as the source domain and EuroSAT and ChestX as target domains.
Qualitative analysis using heatmaps validated the method's feasibility.

Conclusions:

The proposed pseudo-Siamese network effectively leverages contour information for improved cross-domain few-shot learning.
Integrating sketch-based contour features enhances model robustness and performance across different domains.