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.
no code implementations • 19 Jul 2021 • Jie Zhang, Alexandra Brintrup, Anisoara Calinescu, Edward Kosasih, Angira Sharma
This paper explains what is 'twined' in supply chain digital twin and how to 'twin' them to handle the spatio-temporal dynamic issue.
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%.
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).
no code implementations • 2 Nov 2020 • Angira Sharma, Edward Kosasih, Jie Zhang, Alexandra Brintrup, Anisoara Calinescu
This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin.
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%.