Optimal use of attention mechanisms: comparative study in U-Net for image segmentation tasks

Qiangong Zhou; Guanzhang Su; Jiayi Chen; Yuangen Chen; Youyu Zhou

doi:10.1117/12.3021498

19 February 2024 Optimal use of attention mechanisms: comparative study in U-Net for image segmentation tasks

Qiangong Zhou, Guanzhang Su, Jiayi Chen, Yuangen Chen, Youyu Zhou

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130630L (2024) https://doi.org/10.1117/12.3021498
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

In this work, we delve into the implementation approaches of two different classic attention mechanisms and the effects of two distinct methods for adding attention mechanisms in U-Net image segmentation tasks. Based on experiments, we further conclude that in the context of image segmentation tasks in computer vision, attention mechanisms, while achieving weighted channel information, can have a negative impact on the backbone network of conventional convolutional feature extraction. However, they can effectively compensate for the shortcomings when it comes to encoding high-dimensional semantic information in the encoder-decoder model structure.

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

Citation Download Citation

Qiangong Zhou, Guanzhang Su, Jiayi Chen, Yuangen Chen, and Youyu Zhou "Optimal use of attention mechanisms: comparative study in U-Net for image segmentation tasks", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130630L (19 February 2024); https://doi.org/10.1117/12.3021498

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