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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2024

28.10.2023 | Original Article

GCAM: lightweight image inpainting via group convolution and attention mechanism

verfasst von: Yuantao Chen, Runlong Xia, Kai Yang, Ke Zou

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2024

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Abstract

Recently, image inpainting techniques tend to be more concerned with how to enhance the quality of restoration than with how to function on various platforms with limited processing power. In this paper, we propose a lightweight method that combines group convolution and attention mechanism to improve or replace the traditional convolution module. Group convolution was used to achieve multi-level image inpainting, and the authors proposed the rotating attention mechanism for allocation to deal with the issue of information mobility between channels in traditional convolution processing. The parallel discriminator structure was utilized throughout the network's overall design phase to guarantee both local and global consistency of the image inpainting process. The experimental results can demonstrate that, while the quality of image inpainting has been ensured, the proposed image inpainting network's inference time and resource usage are significantly lower than those of comparable lightweight approaches.
Vorheriger Artikel A deep reinforcement learning approach incorporating genetic algorithm for missile path planning
Nächster Artikel Feature selection for label distribution learning under feature weight view

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Metadaten
Titel
GCAM: lightweight image inpainting via group convolution and attention mechanism
verfasst von
Yuantao Chen
Runlong Xia
Kai Yang
Ke Zou
Publikationsdatum
28.10.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2024
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-01999-z

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