We present a simple and practical framework for anomaly segmentation called Maskomaly. It builds upon mask-based standard semantic segmentation networks by adding a simple inference-time post-processing step which leverages the raw mask outputs of such networks. Maskomaly does not require additional training and only adds a small computational overhead to inference. Most importantly, it does not require anomalous data at training. We show top results for our method on SMIYC, RoadAnomaly, and StreetHazards. On the most central benchmark, SMIYC, Maskomaly outperforms all directly comparable approaches. Further, we introduce a novel metric that benefits the development of robust anomaly segmentation methods and demonstrate its informativeness on RoadAnomaly.

翻译：摘要：我们提出了一种名为Maskomaly的简单实用异常分割框架。该框架基于标准掩膜语义分割网络，通过添加一个简单的推理后处理步骤，利用此类网络生成的原始掩膜输出。Maskomaly无需额外训练，仅增加少量推理计算开销。最重要的是，该方法在训练阶段无需异常数据。我们在SMIYC、RoadAnomaly和StreetHazards数据集上取得了领先结果。在最核心的基准测试SMIYC中，Maskomaly在所有直接可比方法中表现最佳。此外，我们引入了一种新型指标，该指标有助于开发鲁棒的异常分割方法，并在RoadAnomaly上验证了其信息有效性。