Paper accepted at ICPR 2022
Our paper titled “Neural Network Pruning Through Constrained Reinforcement Learning” accepted at ICPR 2022.
This work was an outcome of MS Thesis by Shehryar Malik
More info: Click here
Our paper titled “Neural Network Pruning Through Constrained Reinforcement Learning” accepted at ICPR 2022.
This work was an outcome of MS Thesis by Shehryar Malik
More info: Click here
The Computer Vision Lab hosted a rigorous summer internship program for undergraduate students. Sophomore, Junior and Senior interns worked for 2 months in the lab, under the supervision of faculty and PhD students. The students worked both individually and in groups, on a range of ideas, from making a campus 3D model to automatic generation…
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…
Our paper titled “Camera Calibration through Camera Projection Loss” accepted at ICASSP 2022. This work was an outcome of MS Thesis by Talha Hanif Butt More info: Click here
Our paper titled “Stereollax Net: Stereo Parallax Based Deep Learning Network For Building Height Estimation” accepted at IEEE Transactions on Geoscience and Remote Sensing. This work was an outcome of PhD Thesis work by Sana Jabbar More info: Click here
Arif Mahmood successfully defended his Ph.D. thesis on May 27, 2011 at LUMS, Lahore. Thesis Abstract: Template matching is frequently used in Digital Image Processing, Machine Vision, Remote Sensing and Pattern Recognition, and a large number of template matching algorithms have been proposed in literature. The performance of these algorithms may be evaluated from the…
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…