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. Code is available at ${\href{https://github.com/gaozhitong/ATTA}{https://github.com/gaozhitong/ATTA}}$.

翻译：近期在稠密分布外检测方面的进展主要聚焦于训练集与测试集共享相似域的场景，并假设两者间不存在域偏移。然而在实际应用中，域偏移现象普遍存在且会显著影响现有分布外检测模型的准确性。本文提出了一种双层分布外检测框架，用于联合处理域偏移与语义偏移问题。第一层通过全局低级特征判断图像是否存在域偏移，第二层利用稠密高级特征图识别具有语义偏移的像素。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。我们在多个分布外分割基准（包括存在显著域偏移和不存在域偏移的场景）上验证了所提方法的有效性，观察到各种基线模型均能获得一致的性能提升。代码已开源在${\href{https://github.com/gaozhitong/ATTA}{https://github.com/gaozhitong/ATTA}}$。