Realistic to abstract translation of pyramid attention networks

Tai Zhang; Zhongliang Kan

doi:10.1117/12.3038144

7 August 2024 Realistic to abstract translation of pyramid attention networks

Tai Zhang, Zhongliang Kan

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322913 (2024) https://doi.org/10.1117/12.3038144
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Image-to-image unsupervised translation involves the visual level and the graphics level. In its process, the main principle of its performance is to learn the correspondence between two different image sets without using specific image pairs. However, without an image paired data set, under such conditions, if you want to obtain the best possible results, it is difficult to avoid a deeper network structure, resulting in a huge model, which means more calculations. This paper introduces an efficient module using parallel feature extraction, which helps reduce depth while retaining high performance. It is valuable to prioritize the structural aspects of image translation to enhance the coherence and quality of the translated output, thereby mitigating the risk of producing fragmented results. This article uses an attention network - the pyramid attention network, so that it can pay more attention to high-level features of images. The results show that this method is indeed optimized in image translation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tai Zhang and Zhongliang Kan "Realistic to abstract translation of pyramid attention networks", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322913 (7 August 2024); https://doi.org/10.1117/12.3038144

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KEYWORDS

Feature extraction

Image quality

Convolution

Data modeling

Visualization

Education and training

Image processing

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