  Anomaly awareness is an essential capability for safety-critical applications
such as autonomous driving. While recent progress of robotics and computer
vision has enabled anomaly detection for image classification, anomaly
detection on semantic segmentation is less explored. Conventional anomaly-aware
systems assuming other existing classes as out-of-distribution (pseudo-unknown)
classes for training a model will result in two drawbacks. (1) Unknown classes,
which applications need to cope with, might not actually exist during training
time. (2) Model performance would strongly rely on the class selection.
Observing this, we propose a novel Synthetic-Unknown Data Generation, intending
to tackle the anomaly-aware semantic segmentation task. We design a new Masked
Gradient Update (MGU) module to generate auxiliary data along the boundary of
in-distribution data points. In addition, we modify the traditional
cross-entropy loss to emphasize the border data points. We reach the
state-of-the-art performance on two anomaly segmentation datasets. Ablation
studies also demonstrate the effectiveness of proposed modules.

Computer Vision and Pattern Recognition