Semantic segmentation is a prominent problem in scene understanding expressed as a dense labeling task with deep learning models being one of the main methods to solve it. Traditional training algorithms for semantic segmentation models produce less than satisfactory results when not combined with post-processing techniques such as CRFs. In this paper, we propose a method to train segmentation models using an approach which utilizes classification information in the training process of the segmentation network. Our method employs the use of classification network that detects the presence of classes in the segmented output. These class scores are then used to train the segmentation model. This method is motivated by the fact that by conditioning the training of the segmentation model with these scores, higher order features can be captured. Our experiments show significantly improved performance of the segmentation model on the CamVid and CityScapes datasets with no additional post processing.

语义分割是场景理解中的一个突出问题，表现为密集标记任务，深度学习模型是解决该问题的主要方法之一。当不与 CRF 等后处理技术相结合时，传统的语义分割模型训练算法产生的结果不尽人意。在本文中，我们提出了一种使用在分割网络的训练过程中利用分类信息的方法来训练分割模型的方法。我们的方法使用分类网络来检测分段输出中类的存在。然后使用这些类别分数来训练分割模型。这种方法的动机是通过用这些分数调节分割模型的训练，可以捕获更高阶的特征。