Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation [article]

Yingda Xia, Yi Zhang, Fengze Liu, Wei Shen, Alan Yuille
2020 arXiv   pre-print
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,
more » ... 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, i.e., 6 7 StreetHazards anomaly segmentation.
arXiv:2003.08440v2 fatcat:ypqsmpbomjbw3citwwiigdazfq