Anomaly segmentation is a valuable computer vision task for safety-critical applications that need to be aware of unexpected events. Current state-of-the-art (SOTA) scene-level anomaly segmentation approaches rely on diverse inlier class labels during training, limiting their ability to leverage vast unlabeled datasets and pre-trained vision encoders. These methods may underperform in domains with reduced color diversity and limited object classes. Conversely, existing unsupervised methods struggle with anomaly segmentation with the diverse scenes of less restricted domains. To address these challenges, we introduce FlowCLAS, a novel self-supervised framework that utilizes vision foundation models to extract rich features and employs a normalizing flow network to learn their density distribution. We enhance the model's discriminative power by incorporating Outlier Exposure and contrastive learning in the latent space. FlowCLAS significantly outperforms all existing methods on the ALLO anomaly segmentation benchmark for space robotics and demonstrates competitive results on multiple road anomaly segmentation benchmarks for autonomous driving, including Fishyscapes Lost&Found and Road Anomaly. These results highlight FlowCLAS's effectiveness in addressing the unique challenges of space anomaly segmentation while retaining SOTA performance in the autonomous driving domain without reliance on inlier segmentation labels.

翻译：异常分割是一项对安全性要求极高的应用中至关重要的计算机视觉任务，这些应用需要感知意外事件。当前最先进的场景级异常分割方法在训练时依赖于多样化的内点类别标签，这限制了它们利用海量未标注数据集和预训练视觉编码器的能力。这些方法在色彩多样性较低且物体类别有限的领域中可能表现不佳。相反，现有的无监督方法在处理限制较少的多样化场景的异常分割时存在困难。为应对这些挑战，我们提出了FlowCLAS，一种新颖的自监督框架，该框架利用视觉基础模型提取丰富特征，并采用归一化流网络来学习其特征密度分布。我们通过在潜在空间中引入异常暴露和对比学习，增强了模型的判别能力。FlowCLAS在面向空间机器人的ALLO异常分割基准测试中显著优于所有现有方法，并在多个面向自动驾驶的道路异常分割基准测试（包括Fishyscapes Lost&Found和Road Anomaly）中展示了具有竞争力的结果。这些结果突显了FlowCLAS在应对空间异常分割独特挑战方面的有效性，同时在不依赖内点分割标签的情况下，于自动驾驶领域保持了最先进的性能。