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M. Ahmed Bhimra
M. Ahmed Bhimra is a MS Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
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IEEETGRS2024 – Stereollax Net: Stereo Parallax Based Deep Learning Network For Building Height Estimation
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…
ICASSP 2019 – Using 3D Residual Network For Spatio-Temporal Analysis Of Remote Sensing Data
Muhammad Ahmed Bhimra, Usman Nazir, Murtaza Taj International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019 Abstract In this paper, we propose an approach to recognize spatio-temporal changes from remote sensing data. Instead of performing independent analysis on each instance of satellite imagery, we proposed a 3D Convolutional Neural Network…
Waseem Abbas
Waseem Abbas was a Research Assistant in Computer Vision Lab (cvlab) at LUMS Syed Babar Ali School of Science and Engineering. Research Interest Waseem received BSc degree from the Islamia University of Bahawalpur, Pakistan. He received the M.S. degree in Electrical Engineering from the Lahore University of Management Sciences with a focus on machine learning…
Localization Lens for Improving Medical Vision-Language Models
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,…
CATVis: Context-Aware Thought Visualization
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…
ICPR2022 – Neural Network Pruning Through Constrained Reinforcement Learning
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…