Standard unsupervised domain adaptation methods adapt models from a source to a target domain using labeled source data and unlabeled target data jointly. In model adaptation, on the other hand, access to the labeled source data is prohibited, i.e., only the source-trained model and unlabeled target data are available. We investigate normal-to-adverse condition model adaptation for semantic segmentation, whereby image-level correspondences are available in the target domain. The target set consists of unlabeled pairs of adverse- and normal-condition street images taken at GPS-matched locations. Our method -- CMA -- leverages such image pairs to learn condition-invariant features via contrastive learning. In particular, CMA encourages features in the embedding space to be grouped according to their condition-invariant semantic content and not according to the condition under which respective inputs are captured. To obtain accurate cross-domain semantic correspondences, we warp the normal image to the viewpoint of the adverse image and leverage warp-confidence scores to create robust, aggregated features. With this approach, we achieve state-of-the-art semantic segmentation performance for model adaptation on several normal-to-adverse adaptation benchmarks, such as ACDC and Dark Zurich. We also evaluate CMA on a newly procured adverse-condition generalization benchmark and report favorable results compared to standard unsupervised domain adaptation methods, despite the comparative handicap of CMA due to source data inaccessibility. Code is available at https://github.com/brdav/cma.

翻译：标准无监督域自适应方法利用带标签的源域数据和未标签的目标域数据，联合训练模型以适应从源域到目标域的迁移。然而，在模型自适应场景中，标注的源域数据不可访问，即仅能获取源域训练好的模型和未标注的目标域数据。本文研究语义分割任务中"常规-恶劣"条件模型自适应问题，其中目标域存在图像级对应关系：目标数据集包含GPS坐标匹配位置采集的恶劣条件与常规条件的未标注街道图像对。所提方法CMA通过对比学习利用此类图像对学习条件无关特征。具体而言，CMA促使嵌入空间中的特征根据其条件无关的语义内容进行聚类，而非依据输入图像的采集条件。为获取准确的跨域语义对应关系，我们将常规图像扭曲至恶劣图像的视角，并利用扭曲置信度分数构建鲁棒的聚合特征。通过该方法，我们在ACDC和Dark Zurich等多个"常规-恶劣"自适应基准测试中，取得了语义分割模型自适应的最优性能。此外，在新构建的恶劣条件泛化基准测试中，尽管CMA因源数据不可访问而存在比较劣势，其评估结果仍优于标准无监督域自适应方法。代码开源地址：https://github.com/brdav/cma。