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一项基本要求。然而，处理OOD数据的异构性（从低级损坏到语义偏移）仍然是一个复杂的挑战，单阶段检测器通常难以解决。为解决此问题，我们提出了SeNeDiF-OOD，一种基于语义嵌套二分融合的新方法。该框架将检测任务分解为二元融合节点的层次结构，其中每一层旨在整合与特定语义抽象层次对齐的决策边界。为了验证所提出的框架，我们使用MonuMAI（一个暴露于开放环境中的真实世界建筑风格识别系统）进行了全面的案例研究。该应用面临多样化的输入，包括非纪念碑图像、未知建筑风格和对抗性攻击，这使其成为我们方案的理想测试平台。通过在该领域进行广泛的实验评估，结果表明我们的层次融合方法显著优于传统基线，在保持分布内性能的同时，有效地过滤了这些多样化的OOD类别。