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Muhammad Zaigham Zaheer
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2020 – today
- 2024
- [j8]Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada, Seung-Ik Lee
:
Exploiting autoencoder's weakness to generate pseudo anomalies. Neural Comput. Appl. 36(23): 14075-14091 (2024) - [j7]Muhammad Zaigham Zaheer
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos. IEEE Trans. Neural Networks Learn. Syst. 35(10): 14085-14098 (2024) - [c21]Anas Al-lahham, Muhammad Zaigham Zaheer, Nurbek Tastan, Karthik Nandakumar:
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline. CVPR 2024: 12416-12425 - [c20]Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar:
Diffusemix: Label-Preserving Data Augmentation with Diffusion Models. CVPR 2024: 27611-27620 - [c19]Muhammad Saad Saeed, Shah Nawaz, Marta Moscati, Rohan Kumar Das, Muhammad Salman Tahir, Muhammad Zaigham Zaheer, Muhammad Irzam Liaqat, Muhammad Haris Khan, Karthik Nandakumar, Muhammad Haroon Yousaf, Markus Schedl:
A Synopsis of FAME 2024 Challenge: Associating Faces with Voices in Multilingual Environments. ACM Multimedia 2024: 11333-11334 - [c18]Salem AlMarri, Muhammad Zaigham Zaheer, Karthik Nandakumar:
A Multi-Head Approach with Shuffled Segments for Weakly-Supervised Video Anomaly Detection. WACV (Workshops) 2024: 132-142 - [c17]Anas Al-Lahham, Nurbek Tastan, Muhammad Zaigham Zaheer, Karthik Nandakumar:
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection. WACV 2024: 6779-6788 - [i23]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data. CoRR abs/2403.16270 (2024) - [i22]Anas Al-Lahham, Muhammad Zaigham Zaheer, Nurbek Tastan, Karthik Nandakumar:
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline. CoRR abs/2404.00847 (2024) - [i21]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:
Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan. CoRR abs/2404.09342 (2024) - [i20]Marcella Astrid, Muhammad Zaigham Zaheer, Djamila Aouada, Seung-Ik Lee:
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies. CoRR abs/2405.05886 (2024) - [i19]Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar:
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models. CoRR abs/2405.14881 (2024) - [i18]Muhammad Irzam Liaqat, Shah Nawaz, Muhammad Zaigham Zaheer, Muhammad Saad Saeed, Hassan Sajjad, Tom De Schepper, Karthik Nandakumar, Muhammad Haris Khan, Markus Schedl:
Chameleon: Images Are What You Need For Multimodal Learning Robust To Missing Modalities. CoRR abs/2407.16243 (2024) - [i17]Muhammad Saad Saeed, Shah Nawaz, Muhammad Zaigham Zaheer, Muhammad Haris Khan, Karthik Nandakumar, Muhammad Haroon Yousaf, Hassan Sajjad, Tom De Schepper, Markus Schedl:
Modality Invariant Multimodal Learning to Handle Missing Modalities: A Single-Branch Approach. CoRR abs/2408.07445 (2024) - [i16]Asifullah Khan, Anabia Sohail, Mustansar Fiaz, Mehdi Hassan, Tariq Habib Afridi, Sibghat Ullah Marwat, Farzeen Munir, Safdar Ali, Hannan Naseem, Muhammad Zaigham Zaheer, Kamran Ali, Tangina Sultana, Ziaurrehman Tanoli, Naeem Akhter:
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers. CoRR abs/2408.17059 (2024) - 2023
- [j6]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies. Neurocomputing 534: 147-160 (2023) - [j5]Divine Senanu Ametefe
Fingerprint pattern classification using deep transfer learning and data augmentation. Vis. Comput. 39(4): 1703-1716 (2023) - [c16]Muhammad Saad Saeed, Shah Nawaz
Single-branch Network for Multimodal Training. ICASSP 2023: 1-5 - [c15]Vu Ngoc Tu
DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation. ACM Multimedia 2023: 9521-9525 - [i15]Muhammad Saad Saeed, Shah Nawaz, Muhammad Haris Khan, Muhammad Zaigham Zaheer, Karthik Nandakumar, Muhammad Haroon Yousaf, Arif Mahmood:
Single-branch Network for Multimodal Training. CoRR abs/2303.06129 (2023) - [i14]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies. CoRR abs/2303.10704 (2023) - [i13]Vu Ngoc Tu, Van Thong Huynh, Hyung-Jeong Yang, Muhammad Zaigham Zaheer, Shah Nawaz
DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation. CoRR abs/2308.01966 (2023) - [i12]Anas Al-Lahham, Nurbek Tastan, Muhammad Zaigham Zaheer, Karthik Nandakumar:
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detection. CoRR abs/2310.17650 (2023) - 2022
- [j4]Divine Senanu Ametefe
Fingerprint Liveness Detection Schemes: A Review on Presentation Attack. Comput. methods Biomech. Biomed. Eng. Imaging Vis. 10(2): 217-240 (2022) - [j3]Muhammad Zaigham Zaheer
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies. IEEE Trans. Image Process. 31: 5963-5975 (2022) - [c14]Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood:
Face Pyramid Vision Transformer. BMVC 2022: 758 - [c13]Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, Mattia Segù, Fisher Yu, Seung-Ik Lee:
Generative Cooperative Learning for Unsupervised Video Anomaly Detection. CVPR 2022: 14724-14734 - [c12]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Limiting Reconstruction Capability of Autoencoders Using Moving Backward Pseudo Anomalies. UR 2022: 248-251 - [i11]Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, Mattia Segù, Fisher Yu, Seung-Ik Lee:
Generative Cooperative Learning for Unsupervised Video Anomaly Detection. CoRR abs/2203.03962 (2022) - [i10]Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee:
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos. CoRR abs/2203.13704 (2022) - [i9]Muhammad Zaigham Zaheer, Jin Ha Lee, Arif Mahmood, Marcella Astrid, Seung-Ik Lee:
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies. CoRR abs/2203.13716 (2022) - [i8]Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood:
Face Pyramid Vision Transformer. CoRR abs/2210.11974 (2022) - 2021
- [j2]Mustansar Fiaz
4G-VOS: Video Object Segmentation using guided context embedding. Knowl. Based Syst. 231: 107401 (2021) - [c11]Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee:
Learning Not to Reconstruct Anomalies. BMVC 2021: 279 - [c10]Jin Ha Lee, Marcella Astrid
Deep Visual Anomaly Detection with Negative Learning. IW-FCV 2021: 218-232 - [c9]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection. ICCVW 2021: 207-214 - [c8]Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, Marcella Astrid, Seung-Ik Lee:
An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration. ICCVW 2021: 2595-2601 - [c7]Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee:
Domain-Robust Pedestrian-View Intersection Classification. ICTC 2021: 1087-1090 - [i7]Muhammad Zaigham Zaheer, Jin-Ha Lee, Marcella Astrid, Arif Mahmood, Seung-Ik Lee:
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection. CoRR abs/2104.14770 (2021) - [i6]Jin Ha Lee, Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Deep Visual Anomaly detection with Negative Learning. CoRR abs/2105.11058 (2021) - [i5]Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee:
Learning Not to Reconstruct Anomalies. CoRR abs/2110.09742 (2021) - [i4]Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee:
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection. CoRR abs/2110.09768 (2021) - 2020
- [j1]Muhammad Zaigham Zaheer
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels. IEEE Signal Process. Lett. 27: 1705-1709 (2020) - [c6]Jin-Ha Lee, Muhammad Zaigham Zaheer, Marcella Astrid, Seung-Ik Lee:
SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers. CVPR Workshops 2020: 3264-3274 - [c5]Muhammad Zaigham Zaheer, Jin-Ha Lee, Marcella Astrid, Seung-Ik Lee:
Old Is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm. CVPR 2020: 14171-14181 - [c4]Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee:
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection. ECCV (22) 2020: 358-376 - [c3]Marcella Astrid, Jin Ha Lee, Muhammad Zaigham Zaheer, Jae-Yeong Lee, Seung-Ik Lee:
For Safer Navigation: Pedestrian-View Intersection Classification. ICTC 2020: 7-10 - [i3]Muhammad Zaigham Zaheer, Jin Ha Lee, Marcella Astrid, Seung-Ik Lee:
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm. CoRR abs/2004.07657 (2020) - [i2]Muhammad Zaigham Zaheer, Arif Mahmood, Hochul Shin, Seung-Ik Lee:
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels. CoRR abs/2008.11887 (2020) - [i1]Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid, Seung-Ik Lee:
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection. CoRR abs/2011.12077 (2020)
2010 – 2019
- 2019
- [c2]Muhammad Zaigham Zaheer, Jin Ha Lee, Seung-Ik Lee, Beom-Su Seo:
A Brief Survey on Contemporary Methods for Anomaly Detection in Videos. ICTC 2019: 472-473 - [c1]Jin Ha Lee, Ik hee Shin, Sang gu Jeong, Seung-Ik Lee, Muhamamad Zaigham Zaheer, Beom-Su Seo:
Improvement in Deep Networks for Optimization Using eXplainable Artificial Intelligence. ICTC 2019: 525-530
Coauthor Index
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last updated on 2024-11-06 21:36 CET by the dblp team
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