Out-of-distribution (OOD) detection is a fundamental requirement for the reliable deployment of artificial intelligence applications in open-world environments. However, addressing the heterogeneous nature of OOD data, ranging from low-level corruption to semantic shifts, remains a complex challenge that single-stage detectors often fail to resolve. To address this issue, we propose SeNeDiF-OOD, a novel methodology based on Semantic Nested Dichotomy Fusion. This framework decomposes the detection task into a hierarchical structure of binary fusion nodes, where each layer is designed to integrate decision boundaries aligned with specific levels of semantic abstraction. To validate the proposed framework, we present a comprehensive case study using MonuMAI, a real-world architectural style recognition system exposed to an open environment. This application faces a diverse range of inputs, including non-monument images, unknown architectural styles, and adversarial attacks, making it an ideal testbed for our proposal. Through extensive experimental evaluation in this domain, results demonstrate that our hierarchical fusion methodology significantly outperforms traditional baselines, effectively filtering these diverse OOD categories while preserving in-distribution performance.

翻译：分布外（OOD）检测是人工智能应用在开放世界环境中可靠部署的基本要求。然而，OOD数据具有异质性，涵盖从低层级损坏到语义偏移等多种情况，这一复杂挑战往往难以通过单阶段检测器解决。针对此问题，我们提出一种基于语义嵌套二分融合的新方法——SeNeDiF-OOD。该框架将检测任务分解为二元融合节点的层次结构，其中每一层均设计用于整合与特定语义抽象层级对齐的决策边界。为验证所提框架，我们通过MonuMAI系统（一个暴露于开放环境的真实世界建筑风格识别系统）开展了全面案例研究。该应用面临多样化的输入，包括非纪念碑图像、未知建筑风格及对抗攻击，使其成为验证本方法的理想测试平台。在该领域进行的广泛实验评估表明，我们的层次融合方法显著优于传统基线模型，能有效过滤这些多样化的OOD类别，同时保持分布内性能。