VAND2.0 Challenge at CVPR

mcislab无限进步参加CVPR workshop2024 challenge

Hey, this is my baseline for the VAND2.0 Challenge checking if my submission format is right and if the models are running properly.

This makes use of the InvAD model architecture which only trains one model for all mvtec classes

Augmented RealNet (ARNet) with Foreground Extraction for Robust Anomaly Detection

Anomaly Detection with Diffusion Model

We propose to use multiple memory banks comprising normal feature representations to which different perturbations applied in PatchCore.

Leveraging MoE to detect anomalies via text, global, part, and patch features comparison

We propose a robust anomaly detection model to recall normality under domain shift.

VAND 2.0 Challenge at CVPR

We utilize FRE, a fast and principled method to solve the problem of unsupervised visual AD.

Generalized Normality Learning for Robust Anomaly Detection

The most effective model