22 citations · 22 across the 8 of their papers we have counts for
6 papers · 1 filter
ConceptADapt: Concept-guided Adaptive Feature Reconstruction with Dynamic Attention for Few-Shot Industrial Anomaly Detection
Yufei Li, Yicheng Ruan, Long Tian +2
Few-shot industrial anomaly detection (FS-IAD) focuses on detecting and localizing visual defects in industrial inspection during the cold-start phase, where only a limited number…
One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection
Boan Zhang, Wen Li, Guanhua Yu +3
Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under the common assumption that of…
FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection
Long Tian, Yufei Li, Yuyang Dai +3
Few-shot industrial anomaly detection (FS-IAD) presents a critical challenge for practical automated inspection systems operating in data-scarce environments. While existing approa…
Meta-SurDiff: Classification Diffusion Model Optimized by Meta Learning is Reliable for Online Surgical Phase Recognition
Yufei Li, Jirui Wu, Long Tian +4
Online surgical phase recognition has drawn great attention most recently due to its potential downstream applications closely related to human life and health. Despite deep models…
Low-Rank Adaptation of Pre-Trained Stable Diffusion for Rigid-Body Target ISAR Imaging
Boan Zhang, Hang Dong, Jiongge Zhang +5
Traditional range-instantaneous Doppler (RID) methods for rigid-body target imaging often suffer from low resolution due to the limitations of time-frequency analysis (TFA). To add…
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
Ruiying Lu, YuJie Wu, Long Tian +4
Unsupervised image Anomaly Detection (UAD) aims to learn robust and discriminative representations of normal samples. While separate solutions per class endow expensive computation…