115 citations · 187 across the 11 of their papers we have counts for
14 papers
DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation
Vu Ngoc Tu, Van Thong Huynh, Hyung-Jeong Yang +4
Conversational engagement estimation is posed as a regression problem, entailing the identification of the favorable attention and involvement of the participants in the conversati…
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…
Single-branch Network for Multimodal Training
Muhammad Saad Saeed, Shah Nawaz, Muhammad Haris Khan +4
With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text. Researchers have focused on building autonomous…
Face Pyramid Vision Transformer
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood
A novel Face Pyramid Vision Transformer (FPVT) is proposed to learn a discriminative multi-scale facial representations for face recognition and verification. In FPVT, Face Spatial…
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…