“Using 3D Residual Network For Spatio-Temporal Analysis Of Remote Sensing Data”,
“Adaptively Weighted Multi-Task Learning Using Inverse Validation Loss” and
“Point Cloud Segmentation Using Hierarchical Tree for Architectural Models”
have been accepted in “IEEE International Conference on Acoustic Speech and Signal Processing. This conference is going to be held from 12th to 17th May 2019 in Brighton, UK.
Our paper titled “Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection” accepted at AAAI Fall 2023 Symposium on Artificial Intelligence and Climate: The Role of AI in a Climate-Smart Sustainable Future. This work was an outcome of MS Thesis work by Wadood Islam and PhD Thesis work…
Usman Nazir, Numan Khurshid, Muhammad Ahmed Bhimra, Murtaza Taj International Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, June 16-21, 2019 Abstract This paper proposes to employ a Inception-ResNet inspired deep learning architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by identifying brick kilns within “Brick-Kiln-Belt” of South Asia. The framework is developed…
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…
Our paper in ACM Transactions on Graphics April 2012 presents a fundamentally new way to model time-varying point clouds, that dramatically reduces processing times for common graphics pipeline tasks such as labeling, gap-filling, denoising, and motion touch-up. This is joint work with Carnegie Mellon University and Disney Research Pittsburgh, and has been presented at Siggraph 2012.
PhD (Stipend, Fees) 2 x PhD Position in Agriculture Robotics, Machine Learning, Computer Vision 2 x PhD Position in Satellite Imagery, Deep Learning, Image Processing MS (Stipend) 2 x MS Position in Agriculture Robotics, Machine Learning, Computer Vision 2 x MS Position in Satellite Imagery, Deep Learning, Image Processing How To Apply: Email: murtaza.taj@lums.edu.pk Apply Online: https://admissions.lums.edu.pk/
