Alumni | MS Students | People
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…
Alumni | MS Students | People
Inam Ullah Taj
Inam Ullah Taj is a MS Thesis student in Computer Vision & Graphics Lab (cvglab) at LUMS Syed Babar Ali School of Science and Engineering.
MS Thesis Defense: Mohbat Tharani
Challenging the dogma of Relevance Feedback in Content based Image Retrieval Systems with Deep Learning May, 2018 at 3:00 pm in Smart Room 9-105 SBASSE. Abstract Association of images to their content based similar images in a database, is quite a fascinating challenge specially on social media platform where billions of tagged and untagged images…
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…
ICIP2021 – Comprehensive Online Network Pruning via Learnable Scaling Factors
Muhammad Umair Haider and Murtaza Taj Abstract: One of the major challenges in deploying deep neural network architectures is their size which has an adverse effect on their inference time and memory requirements. Deep CNNs can either be pruned width-wise by removing filters or depth-wise by removing layers and blocks. Width wise pruning (filter pruning)…