Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection

Unsupervised anomaly detection (UAD) has evolved from specialized single-class models to unified multi-class models, yet existing multi-class models significantly underperform the most advanced one-for-one counterparts. Moreover, the field has fragmented into specialized methods tailored to specific scenarios (multi-class, 3D, few-shot, etc.), creating deployment barriers and highlighting the need for a unified solution. In this paper, we present Dinomaly2, a unified framework for image UAD. It bridges the performance gap in multi-class models by addressing over-generalization and contextual ambiguity, while extending across diverse data modalities and task settings. Guided by the "less is more" philosophy, we demonstrate that the orchestration of simple elements achieves superior performance in a standard reconstruction-based framework. This methodological minimalism enables natural extension across diverse tasks with minimal task-specific adaptations, establishing that simplicity is the foundation of unification. Extensive experiments on 15 UAD benchmarks demonstrate strong performance across multiple modalities, task settings, and application domains. For example, our unified multi-class model achieves unprecedented 99.9% and 99.3% image-level (I-) AUROC on MVTec-AD and VisA, respectively, with its effectiveness further extending to large-scale datasets containing hundreds of categories. Moreover, using only 8 normal examples per class, our method surpasses previous full-shot models, achieving 98.7% and 97.4%. For multi-view (Real-IAD, MANTA) and multi-modal (MVTec3D, MulSen-AD) inspection, Dinomaly2 demonstrates state-of-the-art performance through lightweight adaptations. The combination of minimalistic design, computational scalability, and broad applicability positions Dinomaly2 as a shared framework for a wide range of real-world anomaly detection applications.