论文标题
合成然后比较：检测语义分割的失败和异常
Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
论文作者
论文摘要
检测故障和异常的能力是构建用于计算机视觉应用的可靠系统的基本要求，尤其是语义细分的安全至关重要的应用，例如自动驾驶和医疗图像分析。在本文中，我们系统地研究了语义分割的失败和异常检测，并提出了一个由两个模块组成的统一框架，以解决这两个相关问题。第一个模块是图像合成模块，该模块从分割布局映射生成合成的图像，第二个是一个比较模块，该模块计算合成图像和输入图像之间的差异。我们在三个具有挑战性的数据集上验证了我们的框架，并通过大幅度的边缘提高了最先进的框架，\ emph {i.e。}，在CityScapes上有6％的AUPR-ERROR，MSD中胰腺肿瘤细分的Pearson相关性为7％，在MSD中进行了20％AUPR，而streethazards aromazards aromaly aromaly aromaly Cheplation Chastecation。
The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image analysis. In this paper, we systematically study failure and anomaly detection for semantic segmentation and propose a unified framework, consisting of two modules, to address these two related problems. The first module is an image synthesis module, which generates a synthesized image from a segmentation layout map, and the second is a comparison module, which computes the difference between the synthesized image and the input image. We validate our framework on three challenging datasets and improve the state-of-the-arts by large margins, \emph{i.e.}, 6% AUPR-Error on Cityscapes, 7% Pearson correlation on pancreatic tumor segmentation in MSD and 20% AUPR on StreetHazards anomaly segmentation.
