Muhammad Zaigham Zaheer

CoRR, 2024

A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection.

[BibT_eX]

[DOI]

Anas Al-Lahham

Nurbek Tastan

Karthik Nandakumar

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023

PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies.

[BibT_eX]

[DOI]

Neurocomputing, 2023

DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation.

[BibT_eX]

[DOI]

Proceedings of the 31st ACM International Conference on Multimedia, 2023

Single-branch Network for Multimodal Training.

[BibT_eX]

[DOI]

Muhammad Saad Saeed

Shah Nawaz

Muhammad Haris Khan

Karthik Nandakumar

Muhammad Haroon Yousaf

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies.

[BibT_eX]

[DOI]

IEEE Trans. Image Process., 2022

Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos.

[BibT_eX]

[DOI]

CoRR, 2022

Limiting Reconstruction Capability of Autoencoders Using Moving Backward Pseudo Anomalies.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Ubiquitous Robots, 2022

Generative Cooperative Learning for Unsupervised Video Anomaly Detection.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Face Pyramid Vision Transformer.

[BibT_eX]

[DOI]

Khawar Islam

Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021

4G-VOS: Video Object Segmentation using guided context embedding.

[BibT_eX]

[DOI]

Mustansar Fiaz

Soon Ki Jung

Knowl. Based Syst., 2021

Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection.

[BibT_eX]

[DOI]

CoRR, 2021

Domain-Robust Pedestrian-View Intersection Classification.

[BibT_eX]

[DOI]

Jae-Yeong Lee

Proceedings of the International Conference on Information and Communication Technology Convergence, 2021

An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Deep Visual Anomaly Detection with Negative Learning.

[BibT_eX]

[DOI]

Jin Ha Lee

Proceedings of the Frontiers of Computer Vision - 27th International Workshop, 2021

Learning Not to Reconstruct Anomalies.

[BibT_eX]

[DOI]

Jae-Yeong Lee

Proceedings of the 32nd British Machine Vision Conference 2021, 2021

2020

A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels.

[BibT_eX]

[DOI]

Hochul Shin

IEEE Signal Process. Lett., 2020

For Safer Navigation: Pedestrian-View Intersection Classification.

[BibT_eX]

[DOI]

Jin Ha Lee

Jae-Yeong Lee

Proceedings of the International Conference on Information and Communication Technology Convergence, 2020

CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2020, 2020

Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm.

[BibT_eX]

[DOI]

Jin-Ha Lee

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers.

[BibT_eX]

[DOI]

Jin-Ha Lee

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

A Brief Survey on Contemporary Methods for Anomaly Detection in Videos.

[BibT_eX]

[DOI]

Jin Ha Lee