  Within the context of autonomous driving, encountering unknown objects
becomes inevitable during deployment in the open world. Therefore, it is
crucial to equip standard semantic segmentation models with anomaly awareness.
Many previous approaches have utilized synthetic out-of-distribution (OoD) data
augmentation to tackle this problem. In this work, we advance the OoD synthesis
process by reducing the domain gap between the OoD data and driving scenes,
effectively mitigating the style difference that might otherwise act as an
obvious shortcut during training. Additionally, we propose a simple fine-tuning
loss that effectively induces a pre-trained semantic segmentation model to
generate a ``none of the given classes" prediction, leveraging per-pixel OoD
scores for anomaly segmentation. With minimal fine-tuning effort, our pipeline
enables the use of pre-trained models for anomaly segmentation while
maintaining the performance on the original task.
