  The inability of state-of-the-art semantic segmentation methods to detect
anomaly instances hinders them from being deployed in safety-critical and
complex applications, such as autonomous driving. Recent approaches have
focused on either leveraging segmentation uncertainty to identify anomalous
areas or re-synthesizing the image from the semantic label map to find
dissimilarities with the input image. In this work, we demonstrate that these
two methodologies contain complementary information and can be combined to
produce robust predictions for anomaly segmentation. We present a pixel-wise
anomaly detection framework that uses uncertainty maps to improve over existing
re-synthesis methods in finding dissimilarities between the input and generated
images. Our approach works as a general framework around already trained
segmentation networks, which ensures anomaly detection without compromising
segmentation accuracy, while significantly outperforming all similar methods.
Top-2 performance across a range of different anomaly datasets shows the
robustness of our approach to handling different anomaly instances.
