Can I read PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress Classification on EtoBox?

PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress Classification by Wenhao Tang; Sheng Huang; Xiaoxian Zhang; Luwen Huangfu is a scholarly article available to read on EtoBox.

What is PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress Classification about?

Automatic pavement distress classification facilitates improving the efficiency of pavement maintenance and reducing the cost of labor and resources. A recently influential branch of this task divides the pavement image into patches and infers the patch labels for addressing these issues from the perspective of multi-instance learning. However, these methods neglect the correlation between patches and suffer from a low efficiency in the model optimization and inference. As a representative approach of vision Transformer, Swin Transformer is able to address both of these issues. It first provides a succinct and efficient framework for encoding the divided patches as visual tokens, then employs self-attention to model their relations. Built upon Swin Transformer, we present a novel vision Transformer named Pavement Image Classification Transformer (PicT) for pavement distress classification. In order to better exploit the discriminative information of pavement images at the patch level, the Patch Labeling Teacher is proposed to leverage a teacher model to dynamically generate pseudo labels of patches from image labels during each iteration, and guides the model to learn the discrimin

Author

Wenhao Tang; Sheng Huang; Xiaoxian Zhang; Luwen Huangfu

Publisher

ACM

Published

2022

Language

EN