Muhammad Zaigham Zaheer

IEEE Trans. Neural Networks Learn. Syst., October, 2024

Exploiting autoencoder's weakness to generate pseudo anomalies.

[BibT_eX]

[DOI]

Djamila Aouada

Neural Comput. Appl., August, 2024

A Survey of the Self Supervised Learning Mechanisms for Vision Transformers.

[BibT_eX]

[DOI]

CoRR, 2024

Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach.

[BibT_eX]

[DOI]

Muhammad Saad Saeed

Muhammad Haris Khan

Hassan Sajjad

Tom De Schepper

Markus Schedl

CoRR, 2024

Chameleon: Images Are What You Need For Multimodal Learning Robust To Missing Modalities.

[BibT_eX]

[DOI]

Muhammad Irzam Liaqat

CoRR, 2024

Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan.

[BibT_eX]

[DOI]

Muhammad Saad Saeed

Muhammad Salman Tahir

Rohan Kumar Das

CoRR, 2024

Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline.

[BibT_eX]

[DOI]

Anas Al-Lahham

Nurbek Tastan

CoRR, 2024

Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data.

[BibT_eX]

[DOI]

CoRR, 2024

A Multi-Head Approach with Shuffled Segments for Weakly-Supervised Video Anomaly Detection.

[BibT_eX]

[DOI]

Salem AlMarri

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

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

[BibT_eX]

[DOI]

Anas Al-Lahham

Nurbek Tastan

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

A Synopsis of FAME 2024 Challenge: Associating Faces with Voices in Multilingual Environments.

[BibT_eX]

[DOI]

Muhammad Salman Tahir

Muhammad Irzam Liaqat

Muhammad Haris Khan

Markus Schedl

Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

Diffusemix: Label-Preserving Data Augmentation with Diffusion Models.

[BibT_eX]

[DOI]

Khawar Islam

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline.

[BibT_eX]

[DOI]

Anas Al-lahham

Nurbek Tastan

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Fingerprint pattern classification using deep transfer learning and data augmentation.

[BibT_eX]

[DOI]

Divine Senanu Ametefe

Suzi Seroja Sarnin

Darmawaty Mohd Ali

Vis. Comput., April, 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

Muhammad Haris Khan

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

Fingerprint Liveness Detection Schemes: A Review on Presentation Attack.

[BibT_eX]

[DOI]

Divine Senanu Ametefe

Suzi Seroja Sarnin

Darmawaty Mohd Ali

Comput. methods Biomech. Biomed. Eng. Imaging Vis., 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