论文标题
语义意识域广义分段
Semantic-Aware Domain Generalized Segmentation
论文作者
论文摘要
当对具有不同数据分布的看不见的目标域进行评估时，经过训练的源域训练的深模型缺乏概括。当我们无法访问目标域样本进行适应时，问题就变得更加明显。在本文中，我们介绍了域通用语义分割，其中训练了分割模型在不使用任何目标域数据的情况下为域不变。解决此问题的现有方法将数据标准化为统一分布。我们认为，尽管这样的标准化促进了全局归一化，但所产生的特征不足以获得明确的分割边界。为了增强类别之间的分离，同时促进了域的不变性，我们提出了一个框架，其中包括两个新的模块：语义意识到标准化（SAN）和语义意识到的美白（SAW）。具体而言，SAN专注于不同图像样式的功能之间的类别级中心对齐，而SAW会为已经与之中心的功能进行分布分布的对齐。在SAN和SAW的帮助下，我们鼓励类别内紧凑性和类别间可分离性。我们通过广泛使用的数据集（即GTAV，Synthia，CityScapes，Mapillary和BDD）进行广泛的实验来验证我们的方法。我们的方法比各种骨干网络上的现有最新技术显示出显着改善。代码可从https://github.com/leolyj/san-saw获得
Deep models trained on source domain lack generalization when evaluated on unseen target domains with different data distributions. The problem becomes even more pronounced when we have no access to target domain samples for adaptation. In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data. Existing approaches to tackle this problem standardize data into a unified distribution. We argue that while such a standardization promotes global normalization, the resulting features are not discriminative enough to get clear segmentation boundaries. To enhance separation between categories while simultaneously promoting domain invariance, we propose a framework including two novel modules: Semantic-Aware Normalization (SAN) and Semantic-Aware Whitening (SAW). Specifically, SAN focuses on category-level center alignment between features from different image styles, while SAW enforces distributed alignment for the already center-aligned features. With the help of SAN and SAW, we encourage both intra-category compactness and inter-category separability. We validate our approach through extensive experiments on widely-used datasets (i.e. GTAV, SYNTHIA, Cityscapes, Mapillary and BDDS). Our approach shows significant improvements over existing state-of-the-art on various backbone networks. Code is available at https://github.com/leolyj/SAN-SAW
