R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection
Abstract 3D anomaly detection plays a crucial role in monitoring parts for localized inherent
defects in precision manufacturing. Embedding-based and reconstruction-based …
defects in precision manufacturing. Embedding-based and reconstruction-based …
Produce once, utilize twice for anomaly detection
S Wang, Q Li, H Luo, C Lv… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Visual anomaly detection aims at classifying and locating the regions that deviate from the
normal appearance. Embedding-based methods and reconstruction-based methods are two …
normal appearance. Embedding-based methods and reconstruction-based methods are two …
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection
Anomaly detection (AD) is a machine learning task that identifies anomalies by learning
patterns from normal training data. In many real-world scenarios, anomalies vary in severity …
patterns from normal training data. In many real-world scenarios, anomalies vary in severity …
CSFIN: A lightweight network for camouflaged object detection via cross-stage feature interaction
Camouflaged object detection (COD) aims to identify the objects that are hidden in their
surroundings, which is a very challenging task due to factors like complex contours and high …
surroundings, which is a very challenging task due to factors like complex contours and high …
Improving Image Anomaly Localization: A Multi-branch and Skip Connection Framework
Image anomaly detection plays an important role in various fields, such as industrial defect
detection and medical image analysis. Although the use of image restoration has led to …
detection and medical image analysis. Although the use of image restoration has led to …
Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that
builds a unified model for multi-class images, serving as an alternative to the conventional …
builds a unified model for multi-class images, serving as an alternative to the conventional …
Hard-Normal Example-Aware Template Mutual Matching for Industrial Anomaly Detection
Anomaly detectors are widely used in industrial manufacturing to detect and localize
unknown defects in query images. These detectors are trained on anomaly-free samples …
unknown defects in query images. These detectors are trained on anomaly-free samples …
Adaptive similarity-guided self-merging network for few-shot semantic segmentation
Y Liu, Y Guo, Y Zhu, M Yu - Computers and Electrical Engineering, 2024 - Elsevier
Abstract Few-shot Semantic Segmentation (FSS) attempts to segment the new category with
only a few labeled samples, presenting a significant challenge. Existing approaches …
only a few labeled samples, presenting a significant challenge. Existing approaches …
DD-Aug: A Knowledge-to-Image Synthetic Data Augmentation Pipeline for Industrial Defect Detection
High-quality and diverse datasets are crucial for supervised learning in industrial defect
detection, yet collecting such data remains challenging. Synthetic data generation offers a …
detection, yet collecting such data remains challenging. Synthetic data generation offers a …
Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection
Unsupervised anomaly detection methods can identify surface defects in industrial images
by leveraging only normal samples for training. Due to the risk of overfitting when learning …
by leveraging only normal samples for training. Due to the risk of overfitting when learning …