Anomaly segmentation plays a pivotal role in identifying atypical objects in images, crucial for hazard detection in autonomous driving systems. While existing methods demonstrate noteworthy results on synthetic data, they often fail to consider the disparity between synthetic and real-world data domains. Addressing this gap, we introduce the Multi-Granularity Cross-Domain Alignment (MGCDA) framework, tailored to harmonize features across domains at both the scene and individual sample levels. Our contributions are twofold: i) We present the Multi-source Domain Adversarial Training module. This integrates a multi-source adversarial loss coupled with dynamic label smoothing, facilitating the learning of domain-agnostic representations across multiple processing stages. ii) We propose an innovative Cross-domain Anomaly-aware Contrastive Learning methodology.} This method adeptly selects challenging anchor points and images using an anomaly-centric strategy, ensuring precise alignment at the sample level. Extensive evaluations of the Fishyscapes and RoadAnomaly datasets demonstrate MGCDA's superior performance and adaptability. Additionally, its ability to perform parameter-free inference and function with various network architectures highlights its distinctiveness in advancing the frontier of anomaly segmentation.

翻译：异常分割在识别图像中的非典型物体方面具有关键作用，对于自动驾驶系统中的危险检测至关重要。现有方法在合成数据上取得了显著成果，但往往忽略了合成数据域与真实数据域之间的差异。针对这一缺陷，我们提出了多粒度跨域对齐（MGCDA）框架，旨在从场景和单个样本两个层面协调不同域之间的特征。我们的贡献体现在两个方面：i) 我们提出了多源域对抗训练模块，该模块结合了多源对抗损失与动态标签平滑技术，有助于在多个处理阶段学习域无关的表示。ii) 我们创新性地提出了一种跨域异常感知对比学习方法，该方法通过异常焦点策略巧妙选择具有挑战性的锚点和图像，确保在样本层面实现精确对齐。在Fishyscapes和RoadAnomaly数据集上的广泛评估表明，MGCDA具有卓越的性能和适应性。此外，其无需参数即可推理的能力以及与多种网络架构的兼容性，进一步凸显了其在推进异常分割前沿研究中的独特价值。