no code implementations • ICCV 2021 • Rahul Venkatesh, Tejan Karmali, Sarthak Sharma, Aurobrata Ghosh, R. Venkatesh Babu, László A. Jeni, Maneesh Singh
Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.
no code implementations • 4 Nov 2020 • Rahul Venkatesh, Sarthak Sharma, Aurobrata Ghosh, Laszlo Jeni, Maneesh Singh
Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.
no code implementations • 11 Aug 2019 • Aurobrata Ghosh, Zheng Zhong, Steve Cruz, Subbu Veeravasarapu, Terrance E. Boult, Maneesh Singh
We propose an innovative method to formulate the issue of localizing manipulated regions in an image as a deep representation learning problem using the Information Bottleneck (IB), which has recently gained popularity as a framework for interpreting deep neural networks.
no code implementations • 31 Jul 2019 • Ryutaro Tanno, Daniel Worrall, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, Alberto Bizzi, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
Here we introduce methods to characterise different components of uncertainty in such problems and demonstrate the ideas using diffusion MRI super-resolution.
no code implementations • 27 Jun 2019 • Aurobrata Ghosh, Zheng Zhong, Terrance E. Boult, Maneesh Singh
It comprises a novel approach for learning rich filters and for suppressing image-edges.
no code implementations • 1 May 2017 • Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
In this work, we investigate the value of uncertainty modeling in 3D super-resolution with convolutional neural networks (CNNs).