25 citations · 72 across the 22 of their papers we have counts for
23 papers
Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning
Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz +1
Deep learning based object detectors struggle generalizing to a new target domain bearing significant variations in object and background. Most current methods align domains by usi…
Leveraging Topology for Domain Adaptive Road Segmentation in Satellite and Aerial Imagery
Javed Iqbal, Aliza Masood, Waqas Sultani +1
Getting precise aspects of road through segmentation from remote sensing imagery is useful for many real-world applications such as autonomous vehicles, urban development and plann…
Detection and Localization of Firearm Carriers in Complex Scenes for Improved Safety Measures
Arif Mahmood, Abdul Basit, M. Akhtar Munir +1
Detecting firearms and accurately localizing individuals carrying them in images or videos is of paramount importance in security, surveillance, and content customization. However,…
Towards Improving Calibration in Object Detection Under Domain Shift
Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz +1
With deep neural network based solution more readily being incorporated in real-world applications, it has been pressing requirement that predictions by such models, especially in…
Distribution Regularized Self-Supervised Learning for Domain Adaptation of Semantic Segmentation
Javed Iqbal, Hamza Rawal, Rehan Hafiz +2
This paper proposes a novel pixel-level distribution regularization scheme (DRSL) for self-supervised domain adaptation of semantic segmentation. In a typical setting, the classifi…
Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised Approach
M. Fasi ur Rehman, Izza Ali, Waqas Sultani +1
One billion people worldwide are estimated to be living in slums, and documenting and analyzing these regions is a challenging task. As compared to regular slums; the small, scatte…
FogAdapt: Self-Supervised Domain Adaptation for Semantic Segmentation of Foggy Images
Javed Iqbal, Rehan Hafiz, Mohsen Ali
This paper presents FogAdapt, a novel approach for domain adaptation of semantic segmentation for dense foggy scenes. Although significant research has been directed to reduce the…
Out of distribution detection for skin and malaria images
Muhammad Zaida, Shafaqat Ali, Mohsen Ali +3
Deep neural networks have shown promising results in disease detection and classification using medical image data. However, they still suffer from the challenges of handling real-…
Towards Low-Cost and Efficient Malaria Detection
Waqas Sultani, Wajahat Nawaz, Syed Javed +3
Malaria, a fatal but curable disease claims hundreds of thousands of lives every year. Early and correct diagnosis is vital to avoid health complexities, however, it depends upon t…
Cross-Region Building Counting in Satellite Imagery using Counting Consistency
Muaaz Zakria, Hamza Rawal, Waqas Sultani +1
Estimating the number of buildings in any geographical region is a vital component of urban analysis, disaster management, and public policy decision. Deep learning methods for bui…
Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
Muhammad Akhtar Munir, Muhammad Haris Khan, M. Saquib Sarfraz +1
We study adapting trained object detectors to unseen domains manifesting significant variations of object appearance, viewpoints and backgrounds. Most current methods align domains…
A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images
Qazi Ammar Arshad, Mohsen Ali, Saeed-ul Hassan +4
Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold-standard for detecting life-threatening disease malari…
Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation
M. Naseer Subhani, Mohsen Ali
Self-supervised learning approaches for unsupervised domain adaptation (UDA) of semantic segmentation models suffer from challenges of predicting and selecting reasonable good qual…
Weakly Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery
Javed Iqbal, Mohsen Ali
This paper proposes a novel domain adaptation algorithm to handle the challenges posed by the satellite and aerial imagery, and demonstrates its effectiveness on the built-up regio…
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…
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling
Javed Iqbal, Mohsen Ali
Most of the recent Deep Semantic Segmentation algorithms suffer from large generalization errors, even when powerful hierarchical representation models based on convolutional neura…
EpO-Net: Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency
Muhammad Faisal, Ijaz Akhter, Mohsen Ali +1
The existing approaches for salient motion segmentation are unable to explicitly learn geometric cues and often give false detections on prominent static objects. We exploit multiv…
Leveraging Orientation for Weakly Supervised Object Detection with Application to Firearm Localization
Javed Iqbal, Muhammad Akhtar Munir, Arif Mahmood +2
Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size, and app…
Deep Built-Structure Counting in Satellite Imagery Using Attention Based Re-Weighting
Anza Shakeel, Waqas Sultani, Mohsen Ali
In this paper, we attempt to address the challenging problem of counting built-structures in the satellite imagery. Building density is a more accurate estimate of the population d…
Automatic Image Transformation for Inducing Affect
Afsheen Rafaqat Ali, Mohsen Ali
Current image transformation and recoloring algorithms try to introduce artistic effects in the photographed images, based on user input of target image(s) or selection of pre-desi…
Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities
Anza Shakeel, Mohsen Ali
Deep convolutional neural networks (CNNs) have outperformed existing object recognition and detection algorithms. On the other hand satellite imagery captures scenes that are diver…
High-Level Concepts for Affective Understanding of Images
Afsheen Rafaqat Ali, Usman Shahid, Mohsen Ali +1
This paper aims to bridge the affective gap between image content and the emotional response of the viewer it elicits by using High-Level Concepts (HLCs). In contrast to previous w…
Comparing Background Subtraction Algorithms and Method of Car Counting
Gautam S. Thakur, Mohsen Ali, Pan Hui +1
In this paper, we compare various image background subtraction algorithms with the ground truth of cars counted. We have given a sample of thousand images, which are the snap shots…