2 papers
cs.CV2026
SwinAD: Multi-stage feature reconstruction for unsupervised industrial anomaly detection
Huong Ninh, Chien Thai, Mai Xuan Trang +3
Industrial anomaly detection aims to identify and localize defective regions without relying on exhaustive annotations of all possible defect types. Although recent unsupervised me…
cs.CV2023
Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers
Hai Phan, Cindy Le, Vu Le +2
Most face identification approaches employ a Siamese neural network to compare two images at the image embedding level. Yet, this technique can be subject to occlusion (e.g. faces…