通过风格对齐的分布外增强实现异常感知语义分割
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation
AI总结：
针对自动驾驶开放场景下语义分割模型的异常感知需求，提出风格对齐的OoD数据增强方法缩小域差距，并结合简单微调损失，使预训练模型仅需少量微调即可实现异常分割且保留原任务性能。
摘要：
在自动驾驶场景下，模型部署于开放世界时不可避免会遇到未知物体，因此为标准语义分割模型赋予异常感知能力至关重要。已有诸多方法利用合成的分布外（OoD）数据增强来解决该问题。本研究改进了OoD合成流程，缩小了OoD数据与驾驶场景之间的域差距，有效缓解了风格差异问题——这类差异原本可能成为训练过程中的明显捷径。此外，我们提出了一种简单的微调损失，利用逐像素OoD分数进行异常分割，有效促使预训练语义分割模型生成“不属于给定类别”的预测。仅需极少的微调工作量，我们的流程就能让预训练模型用于异常分割，同时保持其在原有任务上的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
