13 citations · 18 across the 4 of their papers we have counts for
4 papers · 1 filter
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection
Zhaoxiang Zhang, Hanqiu Deng, Jinan Bao +1
Image Anomaly Detection has been a challenging task in Computer Vision field. The advent of Vision-Language models, particularly the rise of CLIP-based frameworks, has opened new a…
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization
Hanqiu Deng, Xingyu Li
Visual anomaly detection is a challenging open-set task aimed at identifying unknown anomalous patterns while modeling normal data. The knowledge distillation paradigm has shown re…
Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization
Hanqiu Deng, Zhaoxiang Zhang, Jinan Bao +1
Contrastive Language-Image Pre-training (CLIP) models have shown promising performance on zero-shot visual recognition tasks by learning visual representations under natural langua…
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners
Renrui Zhang, Xiangfei Hu, Bohao Li +5
Visual recognition in low-data regimes requires deep neural networks to learn generalized representations from limited training samples. Recently, CLIP-based methods have shown pro…