22 citations · 31 across the 19 of their papers we have counts for
10 papers · 2 filters
Real-IAD Variety: Pushing Industrial Anomaly Detection Dataset to a Modern Era
Wenbing Zhu, Chengjie Wang, Bin-Bin Gao +12
Industrial Anomaly Detection (IAD) is a cornerstone for ensuring operational safety, maintaining product quality, and optimizing manufacturing efficiency. However, the advancement…
Towards Fine-Grained Vision-Language Alignment for Few-Shot Anomaly Detection
Yuanting Fan, Jun Liu, Xiaochen Chen +5
Few-shot anomaly detection (FSAD) methods identify anomalous regions with few known normal samples. Most existing methods rely on the generalization ability of pre-trained vision-l…
DRL: Discriminative Representation Learning with Parallel Adapters for Class Incremental Learning
Jiawei Zhan, Jun Liu, Jinlong Peng +4
With the excellent representation capabilities of Pre-Trained Models (PTMs), remarkable progress has been made in non-rehearsal Class-Incremental Learning (CIL) research. However,…
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection
Bin-Bin Gao, Yue Zhou, Jiangtao Yan +7
Universal visual anomaly detection aims to identify anomalies from novel or unseen vision domains without additional fine-tuning, which is critical in open scenarios. Recent studie…
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning
Bin-Bin Gao
Zero- and few-shot visual anomaly segmentation relies on powerful vision-language models that detect unseen anomalies using manually designed textual prompts. However, visual repre…
Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt
Bin-Bin Gao
Unsupervised reconstruction networks using self-attention transformers have achieved state-of-the-art performance for multi-class (unified) anomaly detection with a single model. H…