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.

本研究提出了一种双层离群分布检测框架，可同时处理领域漂移和语义漂移，通过全局低层特征和密集高层特征图来区分领域漂移和语义漂移，并能够选择性地调整模型以适应未知领域，并提高在检测新类别方面的效果。在多个离群分割基准测试中验证了所提出方法的有效性，包括存在显著领域漂移和不存在领域漂移的情况下，观察到各种基准模型的持续性性能改进。