7 citations · 18 across the 8 of their papers we have counts for
9 papers
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment
Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir +2
In this work, we tackle the problem of domain generalization for object detection, specifically focusing on the scenario where only a single source domain is available. We propose…
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…
Cal-DETR: Calibrated Detection Transformer
Muhammad Akhtar Munir, Salman Khan, Muhammad Haris Khan +2
Albeit revealing impressive predictive performance for several computer vision tasks, deep neural networks (DNNs) are prone to making overconfident predictions. This limits the ado…
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,…
Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Muhammad Akhtar Munir, Muhammad Haris Khan, Salman Khan +1
Deep neural networks (DNNs) have enabled astounding progress in several vision-based problems. Despite showing high predictive accuracy, recently, several works have revealed that…
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…
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…
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…
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…