# Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation Source: [Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:40:08.455Z Authors: Dan Zhang, Kaspar Sakmann, William Beluch, Robin Hutmacher, Yumeng Li Published: 2023-08-19T09:45:39.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()