Semi-supervised Anomaly Detection Edit Task
Computer Vision • Abnormal Event Detection In Video
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Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training
Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal).
Real-world Anomaly Detection in Surveillance Videos
To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i. e. the training labels (anomalous or normal) are at video-level instead of clip-level.
Ranked #2 on
Abnormal Event Detection In Video
on UBI-Fights
ACTIVITY RECOGNITION ANOMALY DETECTION IN SURVEILLANCE VIDEOS MULTIPLE INSTANCE LEARNING SEMI-SUPERVISED ANOMALY DETECTION
Deep Semi-Supervised Anomaly Detection
ICLR 2020
• lukasruff/Deep-SAD-PyTorch
• 
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
ANOMALY DETECTION OUTLIER DETECTION SEMI-SUPERVISED ANOMALY DETECTION
Learning Temporal Regularity in Video Sequences
CVPR 2016
• tnybny/Frame-level-anomalies-in-videos
• 
Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene.
Ranked #2 on
Traffic Accident Detection
on A3D
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
• drsagitn/anomaly-detection-and-localization
•
We present an efficient method for detecting anomalies in videos.
Ranked #4 on
Semi-supervised Anomaly Detection
on UBI-Fights
Semi-supervised Anomaly Detection using AutoEncoders
Journal of Computational Vision and Imaging Systems 2020
• msminhas93/anomaly-detection-using-autoencoders
•
But for defect detection lack of availability of a large number of anomalous instances and labelled data is a problem.
ANOMALY DETECTION DEFECT DETECTION SEMI-SUPERVISED ANOMALY DETECTION
Generative Neural Networks for Anomaly Detection in Crowded Scenes
• tianwangbuaa/VAE-for-abnormal-event-detection
•
Security surveillance is critical to social harmony and people's peaceful life.
Ranked #3 on
Abnormal Event Detection In Video
on UBI-Fights
Iterative weak/self-supervised classification framework for abnormal events detection
• DegardinBruno/human_self_learning_anomaly
•
The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works.
Ranked #1 on
Semi-supervised Anomaly Detection
on UBI-Fights
ANOMALY DETECTION IN SURVEILLANCE VIDEOS DATA AUGMENTATION GENERAL CLASSIFICATION SELF-SUPERVISED ANOMALY DETECTION SELF-SUPERVISED LEARNING SEMI-SUPERVISED ANOMALY DETECTION SEMI-SUPERVISED VIDEO CLASSIFICATION
$\text{A}^3$: Activation Anomaly Analysis
Based on the activation values in the target network, the alarm network decides if the given sample is normal.
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays
• antoine-spahr/SELF-TAUGHT-SEMI-SUPERVISED-ANOMALY-DETECTION
•
Detecting anomalies in musculoskeletal radiographs is of paramount importance for large-scale screening in the radiology workflow.
SELF-SUPERVISED ANOMALY DETECTION SEMI-SUPERVISED ANOMALY DETECTION