论文标题
原型指导网络用于异常分割
Prototype Guided Network for Anomaly Segmentation
论文作者
论文摘要
语义分割方法无法直接识别图像中的异常对象。与此现实设置的异常分割算法可以区分分布对象和分布外（OOD）对象，并输出像素的异常概率。在本文中，提出了一个原型的引导异常分割网络（PGAN）来提取语义原型，以从有限的注释图像中提取分布训练数据。在模型中，原型用于建模分层类别的语义信息并区分OOD像素。提出的PGAN模型包括语义分割网络和原型提取网络。采用相似性措施来优化原型。学习的语义原型用作类别语义，以将相似性与从测试图像中提取的特征进行比较，然后生成语义分割预测。所提出的原型提取网络也可以集成到大多数语义分割网络中并识别OOD像素。在Streethazards数据集上，提出的PGAN模型用于异常分割的MIOU为53.4％。实验结果表明，PGAN可以在异常分割任务中实现SOTA性能。
Semantic segmentation methods can not directly identify abnormal objects in images. Anomaly Segmentation algorithm from this realistic setting can distinguish between in-distribution objects and Out-Of-Distribution (OOD) objects and output the anomaly probability for pixels. In this paper, a Prototype Guided Anomaly segmentation Network (PGAN) is proposed to extract semantic prototypes for in-distribution training data from limited annotated images. In the model, prototypes are used to model the hierarchical category semantic information and distinguish OOD pixels. The proposed PGAN model includes a semantic segmentation network and a prototype extraction network. Similarity measures are adopted to optimize the prototypes. The learned semantic prototypes are used as category semantics to compare the similarity with features extracted from test images and then to generate semantic segmentation prediction. The proposed prototype extraction network can also be integrated into most semantic segmentation networks and recognize OOD pixels. On the StreetHazards dataset, the proposed PGAN model produced mIoU of 53.4% for anomaly segmentation. The experimental results demonstrate PGAN may achieve the SOTA performance in the anomaly segmentation tasks.
