Guided Self-attention: Find the Generalized Necessarily Distinct Vectors for Grain Size Grading

• URL: http://arxiv.org/abs/2410.05762v1
• Date: Tue, 8 Oct 2024 07:40:31 GMT
• Title: Guided Self-attention: Find the Generalized Necessarily Distinct Vectors for Grain Size Grading
• Authors: Fang Gao, Xuetao Li, Jiabao Wang, Shengheng Ma, Jun Yu,
• Abstract summary: We propose a novel classifi-cation method based on deep learning, namely GSNets, which can effectively introduce guided self-attention for classifying grain size. Experiments show that our GSNet yields a classifi-cation accuracy of 90.1%, surpassing the state-of-the-art Swin Transformer V2 by 1.9%. We intuitively believe our approach is applicable to broader ap-plications like object detection and semantic segmentation.
• Score: 11.220653004059304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of steel materials, metallographic analysis has become increasingly important. Unfortunately, grain size analysis is a manual process that requires experts to evaluate metallographic photographs, which is unreliable and time-consuming. To resolve this problem, we propose a novel classifi-cation method based on deep learning, namely GSNets, a family of hybrid models which can effectively introduce guided self-attention for classifying grain size. Concretely, we build our models from three insights:(1) Introducing our novel guided self-attention module can assist the model in finding the generalized necessarily distinct vectors capable of retaining intricate rela-tional connections and rich local feature information; (2) By improving the pixel-wise linear independence of the feature map, the highly condensed semantic representation will be captured by the model; (3) Our novel triple-stream merging module can significantly improve the generalization capability and efficiency of the model. Experiments show that our GSNet yields a classifi-cation accuracy of 90.1%, surpassing the state-of-the-art Swin Transformer V2 by 1.9% on the steel grain size dataset, which comprises 3,599 images with 14 grain size levels. Furthermore, we intuitively believe our approach is applicable to broader ap-plications like object detection and semantic segmentation.

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