Anomaly segmentation plays a crucial role in identifying anomalous objects
within images, which facilitates the detection of road anomalies for autonomous
driving. Although existing methods have shown impressive results in anomaly
segmentation using synthetic training data, the domain discrepancies between
synthetic training data and real test data are often neglected. To address this
issue, the Multi-Granularity Cross-Domain Alignment (MGCDA) framework is
proposed for anomaly segmentation in complex driving environments. It uniquely
combines a new Multi-source Domain Adversarial Training (MDAT) module and a
novel Cross-domain Anomaly-aware Contrastive Learning (CACL) method to boost
the generality of the model, seamlessly integrating multi-domain data at both
scene and sample levels. Multi-source domain adversarial loss and a dynamic
label smoothing strategy are integrated into the MDAT module to facilitate the
acquisition of domain-invariant features at the scene level, through
adversarial training across multiple stages. CACL aligns sample-level
representations with contrastive loss on cross-domain data, which utilizes an
anomaly-aware sampling strategy to efficiently sample hard samples and anchors.
The proposed framework has decent properties of parameter-free during the
inference stage and is compatible with other anomaly segmentation networks.
Experimental conducted on Fishyscapes and RoadAnomaly datasets demonstrate that
the proposed framework achieves state-of-the-art performance.

提出了适用于复杂驾驶环境中异常物体分割的多粒度跨域对齐（MGCDA）框架，该框架将新的多源领域对抗训练（MDAT）模块和新的跨域异常感知对比学习（CACL）方法结合起来，以增强模型的普适性，无缝地整合多域数据，并在场景和样本级别上进行操作。