no code implementations • 9 May 2024 • Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada, Seung-Ik Lee
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 representations of the normal training data.
1 code implementation • 14 Apr 2024 • Muhammad Saad Saeed, Shah Nawaz, Muhammad Salman Tahir, Rohan Kumar Das, Muhammad Zaigham Zaheer, Marta Moscati, Markus Schedl, Muhammad Haris Khan, Karthik Nandakumar, Muhammad Haroon Yousaf
The Face-voice Association in Multilingual Environments (FAME) Challenge 2024 focuses on exploring face-voice association under a unique condition of multilingual scenario.
1 code implementation • 1 Apr 2024 • Anas Al-lahham, Muhammad Zaigham Zaheer, Nurbek Tastan, Karthik Nandakumar
Unsupervised (US) video anomaly detection (VAD) in surveillance applications is gaining more popularity recently due to its practical real-world applications.
no code implementations • 24 Mar 2024 • Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
During test time, since AE is not trained using real anomalies, it is expected to poorly reconstruct the anomalous data.
no code implementations • 19 Mar 2023 • Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
Typically in OCC, an autoencoder (AE) is trained to reconstruct the normal only training data with the expectation that, in test time, it can poorly reconstruct the anomalous data.
1 code implementation • 10 Mar 2023 • Muhammad Saad Saeed, Shah Nawaz, Muhammad Haris Khan, Muhammad Zaigham Zaheer, Karthik Nandakumar, Muhammad Haroon Yousaf, Arif Mahmood
With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text.
1 code implementation • 21 Oct 2022 • 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.
no code implementations • 25 Mar 2022 • Muhammad Zaigham Zaheer, Jin Ha Lee, Arif Mahmood, Marcella Astrid, Seung-Ik Lee
In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions.
no code implementations • 25 Mar 2022 • Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee
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 as the rare occurrence of anomalous events in the training data.
no code implementations • CVPR 2022 • Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, Mattia Segu, Fisher Yu, Seung-Ik Lee
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings.
1 code implementation • 19 Oct 2021 • Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
A popular way to tackle this problem is by utilizing an autoencoder (AE) trained only on normal data.
1 code implementation • 19 Oct 2021 • Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee
Typically, to tackle this problem, an autoencoder (AE) is trained to reconstruct the input with training set consisting only of normal data.
Ranked #19 on Anomaly Detection on CUHK Avenue
no code implementations • 24 May 2021 • Jin-ha Lee, Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
However, these are trained with only normal data and at the test time, given abnormal data as input, may often generate normal-looking output.
no code implementations • 30 Apr 2021 • Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, Arif Mahmood, Seung-Ik Lee
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels.
no code implementations • ECCV 2020 • Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee
The proposed method obtains83. 03% and 89. 67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.
no code implementations • 27 Aug 2020 • Muhammad Zaigham Zaheer, Arif Mahmood, Hochul Shin, Seung-Ik Lee
Anomalous event detection in surveillance videos is a challenging and practical research problem among image and video processing community.
1 code implementation • Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020 • Jin-ha Lee, Muhammad Zaigham Zaheer, Marcella Astrid, Seung-Ik Lee
Data augmentation has been proven effective which, by preventing overfitting, can not only enhances the performance of a deep neural network but also leads to a better generalization even with limited dataset.
1 code implementation • CVPR 2020 • Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid, Seung-Ik Lee
Another possible approach is to use both generator and discriminator for anomaly detection.
Ranked #1 on Anomaly Detection on MNIST-test