63 citations · 107 across the 16 of their papers we have counts for
14 papers · 1 filter
Weight and Height Estimation from a Single Human Image Captured in the Wild
Hira Yaseen, Arif Mahmood, Waqas Sultani
A person's physical characteristics such as weight and height are important indicators of his physical and mental health, daily life routines and finances. Body Mass Index (BMI) is…
Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement
Basit Alawode, Fayaz Ali Dharejo, Mehnaz Ummar +6
This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT). Despite its significance, underwater tracking has remained un…
Learning Structure Aware Deep Spectral Embedding
Hira Yaseen, Arif Mahmood
Spectral Embedding (SE) has often been used to map data points from non-linear manifolds to linear subspaces for the purpose of classification and clustering. Despite significant a…
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification
Sajid Javed, Arif Mahmood, Talha Qaiser +2
Classification of gigapixel Whole Slide Images (WSIs) is an important prediction task in the emerging area of computational pathology. There has been a surge of research in deep le…
Single-branch Network for Multimodal Training
Muhammad Saad Saeed, Shah Nawaz, Muhammad Haris Khan +4
With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text. Researchers have focused on building autonomous…
Face Pyramid Vision Transformer
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood
A novel Face Pyramid Vision Transformer (FPVT) is proposed to learn a discriminative multi-scale facial representations for face recognition and verification. In FPVT, Face Spatial…
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies
Muhammad Zaigham Zaheer, Jin Ha Lee, Arif Mahmood +2
Recently, anomaly scores have been formulated using reconstruction loss of the adversarially learned generators and/or classification loss of discriminators. Unavailability of anom…
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos
Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid +1
Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well…
Quantification of Occlusion Handling Capability of a 3D Human Pose Estimation Framework
Mehwish Ghafoor, Arif Mahmood
3D human pose estimation using monocular images is an important yet challenging task. Existing 3D pose detection methods exhibit excellent performance under normal conditions howev…
Generative Cooperative Learning for Unsupervised Video Anomaly Detection
Muhammad Zaigham Zaheer, Arif Mahmood, Muhammad Haris Khan +3
Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite spars…
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection
Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid +2
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels. An anomalous labelled video may actu…
Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks
Bilal Yousaf, Muhammad Usama, Waqas Sultani +2
Rapid progress in adversarial learning has enabled the generation of realistic-looking fake visual content. To distinguish between fake and real visual content, several detection t…
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid +1
Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work…
Localizing Firearm Carriers by Identifying Human-Object Pairs
Abdul Basit, Muhammad Akhtar Munir, Mohsen Ali +1
Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm). We present a novel approach to ad…