ADNet: 一个大规模且可扩展的多领域异常检测基准，涵盖380个现实世界类别
ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories
AI总结：
ADNet是一个大规模多领域异常检测基准，涵盖380个现实世界类别，通过多模态数据支持异常检测研究。
摘要：
异常检测（AD）旨在使用仅正常样本的训练数据来识别缺陷。现有异常检测基准（例如MVTec-AD，包含15个类别）仅覆盖了有限的类别范围，限制了跨情境泛化和可扩展性的评估。我们引入了ADNet，一个大规模、多领域基准，包含从电子、工业、农业食品、基础设施和医疗领域公开可用的49个数据集中汇总的380个类别。该基准包含总计196,294张RGB图像，包括116,192张用于训练的正常样本和80,102张测试图像，其中60,311张是异常的。所有图像均采用MVTec风格的像素级注释和涵盖空间和视觉属性的结构化文本描述，以支持多模态异常检测任务。广泛的实验揭示了明显的可扩展性挑战：现有最先进的方法在一对一设置中达到90.6%的I-AUROC，但在多类设置中扩展到所有380个类别时下降到78.5%。为了解决这个问题，我们提出了Dinomaly-m，一种基于上下文引导的Mixture-of-Experts扩展，扩展了解码器容量而不增加推理成本。它实现了83.2%的I-AUROC和93.1%的P-AUROC，证明了优于现有方法的性能。ADNet被设计为标准化且可扩展的基准，支持社区在不同领域扩展异常检测数据集，并为未来异常检测基础模型提供可扩展的基础。数据集：https://grainnet.github.io/ADNet
英文摘要：
Anomaly detection (AD) aims to identify defects using normal-only training data. Existing anomaly detection benchmarks (e.g., MVTec-AD with 15 categories) cover only a narrow range of categories, limiting the evaluation of cross-context generalization and scalability. We introduce ADNet, a large-scale, multi-domain benchmark comprising 380 categories aggregated from 49 publicly available datasets across Electronics, Industry, Agrifood, Infrastructure, and Medical domains. The benchmark includes a total of 196,294 RGB images, consisting of 116,192 normal samples for training and 80,102 test images, of which 60,311 are anomalous. All images are standardized with MVTec-style pixel-level annotations and structured text descriptions spanning both spatial and visual attributes, enabling multimodal anomaly detection tasks. Extensive experiments reveal a clear scalability challenge: existing state-of-the-art methods achieve 90.6% I-AUROC in one-for-one settings but drop to 78.5% when scaling to all 380 categories in a multi-class setting. To address this, we propose Dinomaly-m, a context-guided Mixture-of-Experts extension of Dinomaly that expands decoder capacity without increasing inference cost. It achieves 83.2% I-AUROC and 93.1% P-AUROC, demonstrating superior performance over existing approaches. ADNet is designed as a standardized and extensible benchmark, supporting the community in expanding anomaly detection datasets across diverse domains and providing a scalable foundation for future anomaly detection foundation models. Dataset: https://grainnet.github.io/ADNet
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
