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

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 classification 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 relational connections and rich local feature information; 2) By improving the pixelwise 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 classification accuracy of 90.1%, surpassing the state-of-the-art Swin Transformer V2 by 1.9% on the steel grain size dataset, which comprises 3599 images with 14 grain size levels. Furthermore, we intuitively believe our approach is applicable to broader applications such as object detection and semantic segmentation.