SPG: Sparse-Projected Guides with Sparse Autoencoders for Zero-Shot Anomaly Detection

ArXi:2604.02871v1 Announce Type: new We study zero-shot anomaly detection and segmentation using frozen foundation model features, where all learnable parameters are trained only on a labeled auxiliary dataset and deployed to unseen target categories without any target-domain adaptation. Existing prompt-based approaches use handcrafted or learned prompt embeddings as reference vectors for normal/anomalous states.