no code implementations • 6 Oct 2021 • Andrew Gambardella, Bogdan State, Naeemullah Khan, Leo Tsourides, Philip H. S. Torr, Atılım Güneş Baydin
We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm.
no code implementations • NeurIPS Workshop DLDE 2021 • Naeemullah Khan, Angira Sharma, Philip Torr, Ganesh Sundaramoorthi
ST-DNN are deep networks formulated through the use of partial differential equations (PDE) to be defined on arbitrarily shaped regions.
1 code implementation • 16 Jul 2021 • Angira Sharma, Naeemullah Khan, Muhammad Mubashar, Ganesh Sundaramoorthi, Philip Torr
For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.
2 code implementations • 2 Jul 2021 • Motasem Alfarra, Adel Bibi, Naeemullah Khan, Philip H. S. Torr, Bernard Ghanem
Deep neural networks are vulnerable to input deformations in the form of vector fields of pixel displacements and to other parameterized geometric deformations e. g. translations, rotations, etc.
no code implementations • 16 Feb 2021 • Naeemullah Khan, Angira Sharma, Ganesh Sundaramoorthi, Philip H. S. Torr
We stack multiple PDE layers to generalize a deep CNN to arbitrary regions, and apply it to segmentation.
no code implementations • 1 Jan 2021 • Naeemullah Khan, Angira Sharma, Philip Torr, Ganesh Sundaramoorthi
We present Shape-Tailored Deep Neural Networks (ST-DNN).
1 code implementation • 28 Oct 2020 • Angira Sharma, Naeemullah Khan, Ganesh Sundaramoorthi, Philip Torr
For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.
1 code implementation • NeurIPS 2020 • Arslan Chaudhry, Naeemullah Khan, Puneet K. Dokania, Philip H. S. Torr
In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the continual learning experience is finished.
no code implementations • CVPR 2018 • Naeemullah Khan, Ganesh Sundaramoorthi
We formulate and optimize a joint optimization problem in the segmentation and descriptors to discriminate these base descriptors using the learned metric, equivalent to grouping learned descriptors.
no code implementations • CVPR 2017 • Naeemullah Khan, Byung-Woo Hong, Anthony Yezzi, Ganesh Sundaramoorthi
We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions.
no code implementations • 24 Mar 2016 • Ganesh Sundaramoorthi, Naeemullah Khan, Byung-Woo Hong
We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoothing across boundaries of regions.
no code implementations • CVPR 2015 • Naeemullah Khan, Marei Algarni, Anthony Yezzi, Ganesh Sundaramoorthi
Given a region of arbitrary shape in an image, these descriptors are formed from shape-dependent scale spaces of oriented gradients.