
This work proposes a large-scale and general-purpose COCO-AD benchmark for comprehensive evaluation of multi-class anomaly detection methods, and introduces a novel feature inversion framework (InvAD) that achieves state-of-the-art performance on this challenging benchmark as well as other popular datasets.


coremsg

comprehensive-multi-class-anomaly-detection-under-a-general-purpose-coco-ad-benchmark


Comprehensive Multi-class Anomaly Detection under a General-purpose COCO-AD Benchmark


title_rewrite


MambaAD, a novel framework that leverages the Mamba architecture, achieves state-of-the-art performance in multi-class unsupervised anomaly detection tasks while maintaining low model complexity.


mambaad-a-mamba-based-approach-for-efficient-and-effective-multi-class-unsupervised-anomaly-detection


MambaAD: A Mamba-based Approach for Efficient and Effective Multi-class Unsupervised Anomaly Detection
