Paper accepted at ICONIP 2023
Our paper titled “Stereoential Net: Deep Network for Learning Building Height Using Stereo Imagery” accepted at ICONIP 2023.
This work was an outcome of PhD Thesis by Sana Jabbar
More info: Click here
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3xPapers accepted at ICASSP 2019
“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…
2xPapers accepted at ICIP 2021
Two of our papers accepted at ICIP 2021 1. “Spatio-Temporal Crop Classification On Volumetric Data”, More info: Click here 2. “Comprehensive Online Network Pruning via Learnable Scaling Factors”, More info: Click here
Ph.D. Thesis Defense: Ijaz Akhter
Tuesday 20 Nov, 2012 at 5:50 pm in Smart Room 9-105 SSE. Abstract A variety of dynamic objects, such as faces, bodies, and cloth, are represented in computer vision and computer graphics as a collection of moving spatial landmarks. A number of tasks are performed on this type of data such as character animation, motion…
PhD Proposal Defense: Usman Nazir
Learning Socio-economic Indicators from Remote Sensing Data Thursday 12 Sep, 2019 at 03:30 am in CS Smart Room 9-105 SBASSE. Abstract Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries. For example, data on poverty and slavery,…
Open Positions
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/