SelectedWorks - Muhammad Haris Khan

Selected Works of Muhammad Haris Khan

Assistant Professor, Department of Computer Vision, MBZUAI

Dr. Muhammad Haris Khan is an Assistant Professor in the Computer Vision Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

Biography

Dr. Muhammad Haris Khan was a Research Scientist in Inception Institute of Artificial Intelligence (IIAI), UAE. Before joining IIAI, he served as an Assistant Professor at Comsats University Islamabad (CUI), Pakistan for 2 years. Earlier, he was a Post-Doctoral fellow at the University of Nottingham (UoN), UK for 1.5 years. 

He obtained his PhD in Computer Vision from University of Nottingham (UoN), UK, and Masters from University of Sussex, UK. Prior to starting his PhD, he also served as a Lecturer for 3 years at CUI, Pakistan. 

He has authored and co-authored several papers in top-ranked computer vision conferences. His research interests span active topics in computer vision including face-related tasks, domain adaptation, visual tracking and object recognition. He is also a recipient of International Research Excellence Scholarship for his Doctoral study.

Works
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About Muhammad Haris Khan

Dr. Muhammad Haris Khan is an Assistant Professor in the Computer Vision Department at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI).

Biography

Dr. Muhammad Haris Khan was a Research Scientist in Inception Institute of Artificial Intelligence (IIAI), UAE. Before joining IIAI, he served as an Assistant Professor at Comsats University Islamabad (CUI), Pakistan for 2 years. Earlier, he was a Post-Doctoral fellow at the University of Nottingham (UoN), UK for 1.5 years. 

He obtained his PhD in Computer Vision from University of Nottingham (UoN), UK, and Masters from University of Sussex, UK. Prior to starting his PhD, he also served as a Lecturer for 3 years at CUI, Pakistan. 

He has authored and co-authored several papers in top-ranked computer vision conferences. His research interests span active topics in computer vision including face-related tasks, domain adaptation, visual tracking and object recognition. He is also a recipient of International Research Excellence Scholarship for his Doctoral study.

Present	Assistant Professor, Mohamed bin Zayed University of Artificial Intelligence ‐ Department of Computer Vision
Institution Organization Position Type Position Title Time Period to Options Current Position Affiliate my profile with this Institution

Computer Sciences, Artificial Intelligence and Robotics, Statistics and Probability, and Data Science

object recognition, domain adaptation, visual tracking, and face-related tasks

Recipient of International Research Excellence Scholarship for his Doctoral study

http://orcid.org/0000-0001-9746-276X

Journal Articles (3)

Moving objects segmentation using generative adversarial modeling (Maryam Sultana, Arif Mahmood, Thierry Bouwmans, et al.) Neurocomputing (2022)

Moving Objects Segmentation (MOS) is a crucial step in various computer vision applications, such as visual object tracking, autonomous vehicles, human activity analysis, surveillance, and security. Existing MOS approaches suffer from performance degradation due to ...

Fusion and Orthogonal Projection for Improved Face-Voice Association (Muhammad Saad Saeed, Muhammad Haris Khan, Shah Nawaz, et al.) IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (2022)

We study the problem of learning association between face and voice. Prior works adopt pairwise or triplet loss formulations to learn an embedding space amenable for associated matching and verification tasks. Albeit showing some progress, ...

Mask-guided attention network and occlusion-sensitive hard example mining for occluded pedestrian detection (Jin Xie, Yanwei Pang, Muhammad Haris Khan, et al.) IEEE Transactions on Image Processing (2020)

Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are ...

SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection (Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz, et al.) Advances in Neural Information Processing Systems (2021)

We study adapting trained object detectors to unseen domains manifesting significant variations of object appearance, viewpoints and backgrounds. Most current methods align domains by either using image or instance-level feature alignment in an adversarial fashion. ...

An Anomaly Detection System via Moving Surveillance Robots with Human Collaboration (Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, et al.) 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (2021)

Autonomous anomaly detection is a fundamental step in visual surveillance systems, and so we have witnessed great progress in the form of various promising algorithms. Nonetheless, majority of prior algorithms assume static surveillance cameras that ...

Background/foreground separation: guided attention based adversarial modeling (GAAM) versus robust subspace learning methods (Maryam Sultana, Arif Mahmood, Thierry Bouwmans, et al.) Proceedings of the IEEE International Conference on Computer Vision (2021)

Background-Foreground separation and appearance generation is a fundamental step in many computer vision applications. Existing methods like Robust Subspace Learning (RSL) suffer performance degradation in the presence of challenges like bad weather, illumination variations, occlusion, ...

Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer (Omkar Thawakar, Sanath Narayan, Jiale Cao, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022)

State-of-the-art transformer-based video instance segmentation (VIS) approaches typically utilize either single-scale spatio-temporal features or per-frame multi-scale features during the attention computations. We argue that such an attention computation ignores the multi-scale spatio-temporal feature relationships that ...

HM: Hybrid Masking for Few-Shot Segmentation (Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou, et al.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2022)

We study few-shot semantic segmentation that aims to segment a target object from a query image when provided with a few annotated support images of the target class. Several recent methods resort to a feature ...

Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey And Outlook (Sajid Javed, Martin Danelljan, Fahad Shahbaz Khan, et al.) IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and ...

Generative Cooperative Learning for Unsupervised Video Anomaly Detection (M. Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan, et al.) 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)

Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite sparse, likely because anomalies are less frequent in occurrence and usually not well-defined, which ...

Self-Distilled Vision Transformer for Domain Generalization (Maryam Sultana, Muzammal Naseer, Muhammad Haris Khan, et al.) arXiv (2022)

In recent past, several domain generalization (DG) methods have been proposed, showing encouraging performance, however, almost all of them build on convolutional neural networks (CNNs). There is little to no progress on studying the DG ...

Transformers in Medical Imaging: A Survey (Fahad Shamshad, Salman Khan, Syed Waqas Zamir, et al.) arXiv (2022)

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de ...

Visual object tracking with discriminative filters and siamese networks: A survey and outlook (Muhammad Harris Khan, Sajid Javed, Martin Danelljan, et al.) arXiv (2021)

Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and ...

Rich Semantics Improve Few-Shot Learning (Mohamed Afham, Salman Khan, Muhammad Haris Khan, et al.) arXiv (2021)

Human learning benefits from multi-modal inputs that often appear as rich semantics (e.g., description of an object’s attributes while learning about it). This enables us to learn generalizable concepts from very limited visual examples. However, ...

Low light image enhancement via global and local context modeling (Aditya Arora, Muhammad Haris, Syed Waqas Zamir, et al.) arXiv (2021)

Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNNs) perform well in enhancing images. However, being solely reliant on confined fixed primitives ...

Low light image enhancement via global and local context modeling (Muhammad Harris Khan, Aaditya Arora, Syed Waqas Zamir, et al.) arXiv (2021)

Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNNs) perform well in enhancing images. However, being solely reliant on confined fixed primitives ...