用引导自注意力提升钢晶粒分级准确率，达90.1%。
Guided Self-attention: Find the Generalized Necessarily Distinct Vectors for Grain Size Grading
- 设计引导自注意力模块，捕捉局部特征与关系
- 在3599张图像上实现90.1%分类准确率，超Swin Transformer V2 1.9%
- 方法可推广至目标检测与语义分割等任务
随着钢材发展，金相分析愈发重要。但晶粒尺寸分析依赖人工专家评估金相图片，效率低且不可靠。为此，本文提出基于深度学习的GSNets分类方法，通过三个关键设计：(1) 引入新型引导自注意力模块，帮助模型识别具有普遍性且差异显著的向量，保留复杂关系与丰富局部特征；(2) 提升特征图像素级线性独立性，增强语义表征浓缩能力；(3) 设计三流融合模块，显著提升模型泛化能力与效率。实验表明，在包含3,599张图像、14个晶粒等级的钢晶粒数据集上，GSNet分类准确率达90.1%，超越当前最优的Swin Transformer V2 1.9%。我们认为该方法还可拓展至目标检测与语义分割等场景。
原文摘要 · Abstract (English)
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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