Recent advancements in dense out-of-distribution (OOD) detection have primarily focused on scenarios where the training and testing datasets share a similar domain, with the assumption that no domain shift exists between them. However, in real-world situations, domain shift often exits and significantly affects the accuracy of existing out-of-distribution (OOD) detection models. In this work, we propose a dual-level OOD detection framework to handle domain shift and semantic shift jointly. The first level distinguishes whether domain shift exists in the image by leveraging global low-level features, while the second level identifies pixels with semantic shift by utilizing dense high-level feature maps. In this way, we can selectively adapt the model to unseen domains as well as enhance model's capacity in detecting novel classes. We validate the efficacy of our proposed method on several OOD segmentation benchmarks, including those with significant domain shifts and those without, observing consistent performance improvements across various baseline models.

翻译：近来，密集分布外（OOD）检测的进展主要聚焦于训练和测试数据集共享相似域的场景，并假设两者之间不存在域偏移。然而，在实际场景中，域偏移经常出现并显著影响现有分布外检测模型的准确性。本文提出一种双层分布外检测框架，以联合处理域偏移与语义偏移。第一层通过利用全局低阶特征判断图像中是否存在域偏移，第二层则通过利用密集高阶特征图识别存在语义偏移的像素。通过这种方式，我们既能选择性地使模型适应未见域，又能增强模型检测新类别的能力。我们在多个OOD分割基准数据集（包括存在显著域偏移及不存在域偏移的数据集）上验证了所提方法的有效性，观察到其在多种基线模型上均有一致的性能提升。