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UGainS: Uncertainty Guided Anomaly Instance Segmentation
A single unexpected object on the road can cause an accident or may lead to injuries. To prevent this, we need a reliable mechanism for finding...
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Unsupervised intrusion detection for rail transit based on anomaly segmentation
Detecting intrusions in rail transit can be challenging using traditional supervised methods, as they only detect target categories present in the...
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Enhancing the accuracy of prototype learning in road anomaly segmentation by adding adversarial perturbations to data
Regardless of how many classes a machine learning model had seen during the training procedure, it is inevitable that unexpected and unknown objects...
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Revisiting segmentation-guided denoising student–teacher in anomaly detection
Anomaly detection is a critical issue that needs to be addressed in large-scale industrial manufacturing. DeSTSeg integrates a pre-trained teacher...
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Unsupervised Liver Tumor Segmentation with Pseudo Anomaly Synthesis
Liver lesion segmentation is a challenging task. Liver lesions often appear as regional heterogeneity in various shapes and intensities, while...
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Feature-Based Pipeline for Improving Unsupervised Anomaly Segmentation on Medical Images
Unsupervised methods for anomaly segmentation are promising for computer-aided diagnosis since they can increase the robustness of medical systems...
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Self-supervised Augmented Patches Segmentation for Anomaly Detection
In this paper, our goal is to detect unknown defects in high-resolution images in the absence of anomalous data. Anomaly detection is usually...
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MÆIDM: multi-scale anomaly embedding inpainting and discrimination for surface anomaly detection
The detection of anomalous structures in natural image data plays a crucial role in numerous tasks in the field of computer vision. Methods based on...
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Transformer Based Models for Unsupervised Anomaly Segmentation in Brain MR Images
The quality of patient care associated with diagnostic radiology is proportionate to a physician’s workload. Segmentation is a fundamental limiting...
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Wavelet-SVDD: Anomaly Detection and Segmentation with Frequency Domain Attention
Anomaly detection is a formidable challenge that entails the formulation of a model capable of detecting anomalous patterns in datasets, even when...
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Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI
Unsupervised anomaly segmentation aims to detect patterns that are distinct from any patterns processed during training, commonly called abnormal or...
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Self-supervised Diffusion Model for Anomaly Segmentation in Medical Imaging
A powerful mechanism for detecting anomalies in a self-supervised manner was demonstrated by model training on normal data, which can then be used as...
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U-Flow: A U-Shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold
In this work, we propose a one-class self-supervised method for anomaly segmentation in images that benefits from both a modern machine learning...
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Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task...
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Rethinking Polyp Segmentation From An Out-of-distribution Perspective
Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but...
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Unsupervised Anomaly Segmentation for Brain Lesions Using Dual Semantic-Manifold Reconstruction
Unsupervised anomaly segmentation (UAS) is promising in many computer vision applications, e.g., the analysis of brain MRI, thanks to the advantage...
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Deep Industrial Image Anomaly Detection: A Survey
The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a...
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Exploiting CNN’s visual explanations to drive anomaly detection
Nowadays, deep learning is a key technology for many applications in the industrial area such as anomaly detection. The role of Machine Learning (ML)...
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A weakly supervised anomaly detection method based on deep anomaly scoring network
Recently most anomaly detection methods mainly use normal samples or unlabeled data for training. Due to the lack of prior anomaly knowledge, normal...
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SPot-the-Difference Self-supervised Pre-training for Anomaly Detection and Segmentation
Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we present a new dataset as well as a new self-supervised...