FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection
Few-shot industrial anomaly detection (FS-IAD) presents a critical challenge for practical automated inspection systems operating in data-scarce environments. While existing approaches predominantly focus on deriving prototypes from limited normal samples, they typically neglect to systematically incorporate query image statistics to enhance prototype representativeness. To address this issue, we propose FastRef, a novel and efficient prototype refinement framework for FS-IAD. Our method operates through an iterative two-stage process: (1) characteristic transfer from query features to prototypes via an optimizable transformation matrix, and (2) anomaly suppression through prototype alignment. The characteristic transfer is achieved through linear reconstruction of query features from prototypes, while the anomaly suppression addresses a key observation in FS-IAD that unlike conventional IAD with abundant normal prototypes, the limited-sample setting makes anomaly reconstruction more probable. Therefore, we employ optimal transport (OT) for non-Gaussian sampled features to measure and minimize the gap between prototypes and their refined counterparts for anomaly suppression. For comprehensive evaluation, we integrate FastRef with three competitive prototype-based FS-IAD methods: PatchCore, FastRecon, WinCLIP, and AnomalyDINO. Extensive experiments across four benchmark datasets of MVTec, ViSA, MPDD and RealIAD demonstrate both the effectiveness and computational efficiency of our approach under 1/2/4-shots.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionComputational EfficiencyMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Prototype Refinement Network for Few-Shot Segmentation
Few-shot segmentation targets to segment new classes with few annotated images provided. It is more challenging than traditional semantic segmentation tasks that segment known classes with abundant annotated images. In t…
SegmentationSemantic SegmentationFewmatch: Dynamic Prototype Refinement for Semi-Supervised Few-Shot Learning
Semi-Supervised Few-shot Learning (SS-FSL) investigates the benefit of incorporating unlabelled data in few-shot settings. Recent work has relied on the popular Semi-Supervised Learning (SSL) concept of iterative pseudo-…
Few-Shot LearningPseudo LabelBayPrAnoMeta: Bayesian Proto-MAML for Few-Shot Industrial Image Anomaly Detection
Industrial image anomaly detection is a challenging problem owing to extreme class imbalance and the scarcity of labeled defective samples, particularly in few-shot settings. We propose BayPrAnoMeta, a Bayesian generaliz…
Anomaly DetectionDecoupled Prototype Matching with Vision Foundation Models for Few-Shot Industrial Object Detection
Industrial object detection systems typically rely on large annotated datasets, which are expensive to collect and challenging to maintain in industrial scenarios where the inventory of objects changes frequently. This w…
Few-Shot Object Detection2D Object DetectionPose EstimationTransductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinement
Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared with inductive few-shot learning, transductive models typically perform better as they leverage all samples of the query set. The …
Few-Shot Learninggraph construction