中圖分類號： TP389.1
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2021.07.011
引用格式： 曹露濛，楊周旺. 一種應用于語義分割的新型親和力監督方法[J].信息技術與網絡安全，2021，40(7)：66-71.

A new affinity supervision for semantic segmentation

Abstract： Semantic segmentation is a basic and non-trivial task in computer vision. Many previous work focus on utilizing attention mechanisms and non-local models. In attention mechanisms, affinity matrix is very important. Traditional affinity matrix is an essential part of attention models embedded into the neural networks, used as feature fusion weights. In this paper, the affinity matrix is directly used out of attention mechanisms, and it is associated with labels. Firstly, the label affinity matrix is defined, and then the feature affinity is computed on the output layer of semantic segmentations. Finally, with such two affinities, a novel loss function is defined, which uses label affinities as a kind of structural supervision to help train segmentation networks. Extensive experiments on NYUv2 datasets demonstrate that the loss is effective in promoting semantic segmentation networks.

Key words : semantic segmentation；convolutional neural network；affinity matrix；self-attention