Muhammad Zaigham Zaheer

Orcid: 0000-0001-8272-1351

According to our database1, Muhammad Zaigham Zaheer authored at least 37 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos.
IEEE Trans. Neural Networks Learn. Syst., October, 2024

Exploiting autoencoder's weakness to generate pseudo anomalies.
Neural Comput. Appl., August, 2024

GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing.
CoRR, 2024

A Survey of the Self Supervised Learning Mechanisms for Vision Transformers.
CoRR, 2024

Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach.
CoRR, 2024

Chameleon: Images Are What You Need For Multimodal Learning Robust To Missing Modalities.
CoRR, 2024

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

Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline.
CoRR, 2024

Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data.
CoRR, 2024

A Multi-Head Approach with Shuffled Segments for Weakly-Supervised Video Anomaly Detection.
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.
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.
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.
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.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023
Fingerprint pattern classification using deep transfer learning and data augmentation.
Vis. Comput., April, 2023

PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies.
Neurocomputing, 2023

DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

Single-branch Network for Multimodal Training.
Proceedings of the IEEE International Conference on Acoustics, 2023

2022
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies.
IEEE Trans. Image Process., 2022

Fingerprint Liveness Detection Schemes: A Review on Presentation Attack.
Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2022

Limiting Reconstruction Capability of Autoencoders Using Moving Backward Pseudo Anomalies.
Proceedings of the 19th International Conference on Ubiquitous Robots, 2022

Generative Cooperative Learning for Unsupervised Video Anomaly Detection.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Face Pyramid Vision Transformer.
Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021
4G-VOS: Video Object Segmentation using guided context embedding.
Knowl. Based Syst., 2021

Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection.
CoRR, 2021

Domain-Robust Pedestrian-View Intersection Classification.
Proceedings of the International Conference on Information and Communication Technology Convergence, 2021

An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration.
Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection.
Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Deep Visual Anomaly Detection with Negative Learning.
Proceedings of the Frontiers of Computer Vision - 27th International Workshop, 2021

Learning Not to Reconstruct Anomalies.
Proceedings of the 32nd British Machine Vision Conference 2021, 2021

2020
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels.
IEEE Signal Process. Lett., 2020

For Safer Navigation: Pedestrian-View Intersection Classification.
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.
Proceedings of the Computer Vision - ECCV 2020, 2020

Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm.
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.
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.
Proceedings of the 2019 International Conference on Information and Communication Technology Convergence, 2019

Improvement in Deep Networks for Optimization Using eXplainable Artificial Intelligence.
Proceedings of the 2019 International Conference on Information and Communication Technology Convergence, 2019


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