Papers Weakly-supervised Anomaly Detection
“Weakly-supervised Anomaly Detection” 태그가 달린 논문 32편 · 필터 해제
Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation
Anomaly detection is a practical and challenging task due to the scarcity of anomaly samples in industrial inspection. Some existing anomaly detection methods address this issue by synthesizing anomalies with noise or ex…
Anomaly ClassificationAnomaly DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications
Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling t…
Anomaly Detection In Surveillance VideosContrastive LearningMultiple Instance LearningSupervised Anomaly Detection+3Take Package as Language: Anomaly Detection Using Transformer
Network data packet anomaly detection faces numerous challenges, including exploring new anomaly supervision signals, researching weakly supervised anomaly detection, and improving model interpretability. This paper prop…
Anomaly DetectionIntrusion DetectionLanguage ModelingLanguage Modelling+5Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Network
The widespread implementation of urban surveillance systems has necessitated more sophisticated techniques for anomaly detection to ensure enhanced public safety. This paper presents a significant advancement in the fiel…
Anomaly DetectionAnomaly Detection In Surveillance VideosMultiple Instance LearningSupervised Anomaly Detection+2MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance Videos
Detection of anomaly events is relevant for public safety and requires a combination of fine-grained motion information and contextual events at variable time-scales. To this end, we propose a Multi-Timescale Feature Lea…
Anomaly DetectionAnomaly Detection In Surveillance VideosSupervised Anomaly DetectionVideo Anomaly Detection+1Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
Anomaly detection is a crucial task in various domains. Most of the existing methods assume the normal sample data clusters around a single central prototype while the real data may consist of multiple categories or subg…
Anomaly DetectionContrastive LearningSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionWeakly-supervised anomaly detection for multimodal data distributions
Weakly-supervised anomaly detection can outperform existing unsupervised methods with the assistance of a very small number of labeled anomalies, which attracts increasing attention from researchers. However, existing we…
Anomaly DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionPancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models
Despite the advances in medicine, cancer has remained a formidable challenge. Particularly in the case of pancreatic tumors, characterized by their diversity and late diagnosis, early detection poses a significant challe…
Anomaly DetectionDenoisingSupervised Anomaly DetectionTumor Segmentation+1IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study
Minimizing the need for pixel-level annotated data to train PET lesion detection and segmentation networks is highly desired and can be transformative, given time and cost constraints associated with expert annotations. …
Anomaly DetectionAnomaly SegmentationcounterfactualLesion Detection+2Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
Anomaly detection (AD) plays a pivotal role in numerous web-based applications, including malware detection, anti-money laundering, device failure detection, and network fault analysis. Most methods, which rely on unsupe…
Anomaly DetectionMalware DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionWeakly Supervised Anomaly Detection for Chest X-Ray Image
Chest X-Ray (CXR) examination is a common method for assessing thoracic diseases in clinical applications. While recent advances in deep learning have enhanced the significance of visual analysis for CXR anomaly detectio…
Anomaly DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionRoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies…
Anomaly DetectionSemi-supervised Anomaly DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionLearning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
Video anomaly detection under weak supervision presents significant challenges, particularly due to the lack of frame-level annotations during training. While prior research has utilized graph convolution networks and se…
Anomaly DetectionAnomaly Detection In Surveillance VideosVideo Anomaly DetectionWeakly-supervised Anomaly Detection+1Weakly-Supervised Anomaly Detection in the Milky Way
Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification …
Anomaly DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionWeakly Supervised Detection of Baby Cry
Detection of baby cries is an important part of baby monitoring and health care. Almost all existing methods use supervised SVM, CNN, or their varieties. In this work, we propose to use weakly supervised anomaly detectio…
Anomaly DetectionSupervised Anomaly DetectionWeakly-supervised Anomaly DetectionFew-shot Weakly-supervised Cybersecurity Anomaly Detection
With increased reliance on Internet based technologies, cyberattacks compromising users' sensitive data are becoming more prevalent. The scale and frequency of these attacks are escalating rapidly, affecting systems and …
Anomaly DetectionData AugmentationRepresentation LearningSupervised Anomaly Detection+1Weakly Supervised Anomaly Detection: A Survey
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, a…
Anomaly DetectionSupervised Anomaly DetectionSurveyTime Series+2Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detection
Video anomaly detection is recently formulated as a multiple instance learning task under weak supervision, in which each video is treated as a bag of snippets to be determined whether contains anomalies. Previous effort…
Anomaly DetectionMultiple Instance LearningSupervised Anomaly DetectionVideo Anomaly Detection+1Consistency-based Self-supervised Learning for Temporal Anomaly Localization
This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available during training. In particular, we deal w…
Anomaly Detection In Surveillance VideosAnomaly LocalizationSelf-Supervised LearningSupervised Anomaly Detection+2Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary la…
Anomaly DetectionMeta-LearningMissing LabelsSupervised Anomaly Detection+1