115 citations · 191 across the 12 of their papers we have counts for
16 papers
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada +1
Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or repr…
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data
Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
In order to devise an anomaly detection model using only normal training data, an autoencoder (AE) is typically trained to reconstruct the data. As a result, the AE can extract nor…
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
Due to the rarity of anomalous events, video anomaly detection is typically approached as one-class classification (OCC) problem. Typically in OCC, an autoencoder (AE) is trained t…
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies
Muhammad Zaigham Zaheer, Jin Ha Lee, Arif Mahmood +2
Recently, anomaly scores have been formulated using reconstruction loss of the adversarially learned generators and/or classification loss of discriminators. Unavailability of anom…
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos
Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid +1
Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well…
Generative Cooperative Learning for Unsupervised Video Anomaly Detection
Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan +3
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite spars…
Learning Not to Reconstruct Anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee +1
Video anomaly detection is often seen as one-class classification (OCC) problem due to the limited availability of anomaly examples. Typically, to tackle this problem, an autoencod…
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection
Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by uti…
Deep Visual Anomaly detection with Negative Learning
Jin-Ha Lee, Marcella Astrid, Muhammad Zaigham Zaheer +1
With the increase in the learning capability of deep convolution-based architectures, various applications of such models have been proposed over time. In the field of anomaly dete…
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection
Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid +2
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels. An anomalous labelled video may actu…
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid +1
Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work…
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels
Muhammad Zaigham Zaheer, Arif Mahmood, Hochul Shin +1
Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community. Compared to the frame-level annotations…
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid +1
A popular method for anomaly detection is to use the generator of an adversarial network to formulate anomaly scores over reconstruction loss of input. Due to the rare occurrence o…
Small Object Detection using Context and Attention
Jeong-Seon Lim, Marcella Astrid, Hyun-Jin Yoon +1
There are many limitations applying object detection algorithm on various environments. Especially detecting small objects is still challenging because they have low resolution and…
Rank Selection of CP-decomposed Convolutional Layers with Variational Bayesian Matrix Factorization
Marcella Astrid, Seung-Ik Lee, Beom-Su Seo
Convolutional Neural Networks (CNNs) is one of successful method in many areas such as image classification tasks. However, the amount of memory and computational cost needed for C…
CP-decomposition with Tensor Power Method for Convolutional Neural Networks Compression
Marcella Astrid, Seung-Ik Lee
Convolutional Neural Networks (CNNs) has shown a great success in many areas including complex image classification tasks. However, they need a lot of memory and computational cost…