Hasan Farooq, Murtaza Taj, Mehwish Nasim, Arif Mahmood Abstract: Medical Vision-Language Models (Med-VLMs) have demonstrated strong capabilities in clinical tasks. However, they often struggle to understand anatomical structures and spatial positioning, which are crucial for medical reasoning. To address this, we propose a localization-aware enhancement to the Med-VLM pipeline, introducing improvements at three levels: data,…
Tariq Mehmood*, Hamza Ahmad*, Muhammad Haroon Shakeel, Murtaza Taj (* contributed equally) Abstract: EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage…
Faisal Yaseen, Murtaza Taj, Resmi Ravindran, Fareed Zaffar, Paul A. Luciw, Aamer Ikram, Saerah Iffat Zafar, Tariq Gill, Michael Hogarth and Imran H. Khan Abstract: Background Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological…
Sana Jabbar and Murtaza Taj Abstract: Accurate estimation of building heights is crucial for effective urban planning and resource management as it provides essential geometric information about the urban landscape. Many end-to-end deep learning-based networks have been proposed for image-to-height mapping using high-resolution nonoptical and optical remote sensing imagery. In this study, we develop a…
Usman Nazir, Wadood Islam, Sara Khalid, Murtaza Taj Abstract: Land-use monitoring is fundamental for spatial planning, particularly in view of compound impacts of growing global populations and climate change. Despite existing applications of deep learning in land use monitoring, standard convolutional kernels in deep neural networks limit the applications of these networks to the Euclidean…
Sana Jabbar, Murtaza Taj Abstract: Height estimation plays a crucial role in the planning and assessment of urban development, enabling effective decision-making and evaluation of urban built areas. Accurate estimation of building heights from remote sensing optical imagery poses significant challenges in preserving both the overall structure of complex scenes and the elevation details of…
Shehryar Malik, Muhammad Umair Haider*, Omer Iqbal, Murtaza Taj Abstract: Network pruning reduces the size of neural networks by removing (pruning) neurons such that the performance drop is minimal. Traditional pruning approaches focus on designing metrics to quantify the usefulness of a neuron which is often quite tedious and sub-optimal. More recent approaches have instead…
Talha Hanif Butt, Murtaza Taj Abstract: Camera calibration is a necessity in various tasks including 3D reconstruction, hand-eye coordination for a robotic interaction, autonomous driving, etc. In this work we propose a novel method to predict extrinsic (baseline, pitch, and translation), intrinsic (focal length and principal point offset) parameters using an image pair. Unlike existing…
Shaiq Munir Malik, Fnu Mohbat, Muhammad Umair Haider, Muhammad Musab Rasheed and Murtaza Taj Abstract: To reduce the overwhelming size of Deep Neural Networks, teacher-student techniques aim to transfer knowledge from a complex teacher network to a simple student network. We instead propose a novel method called the teacher-class network consisting of a single teacher…
M. U. Qadeer, S. Saeed, M. Taj and A. Muhammad Abstract: Large-area crop classification using multi-spectral imagery is a widely studied problem for several decades and is generally addressed using classical Random Forest classifier. Recently, deep convolutional neural networks (DCNN) have been proposed. However, these methods only achieved results comparable with Random Forest. In this…
