no code implementations • 15 Oct 2024 • Givi Meishvili, James Clemoes, Charlie Hewitt, Zafiirah Hosenie, Xian Xiao, Martin de La Gorce, Tibor Takacs, Tadas Baltrusaitis, Antonio Criminisi, Chyna McRae, Nina Jablonski, Marta Wilczkowiak
We instead choose a classification approach which can represent the diversity of hairstyles required for a truly robust and inclusive system.
no code implementations • 8 Oct 2022 • Rawal Khirodkar, Brandon Smith, Siddhartha Chandra, Amit Agrawal, Antonio Criminisi
Ensemble approaches for deep-learning-based semantic segmentation remain insufficiently explored despite the proliferation of competitive benchmarks and downstream applications.
Ranked #10 on
Semantic Segmentation
on PASCAL Context
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 • 10 Dec 2018 • Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache
In this paper, we propose to use both types of training data (fully-annotated and weakly-annotated) to train a deep learning model for segmentation.
no code implementations • 23 Jul 2018 • Pawel Mlynarski, Hervé Delingette, Antonio Criminisi, Nicholas Ayache
Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences.
1 code implementation • ICLR 2019 • Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya Nori
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.
no code implementations • ICML 2018 • Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.
3 code implementations • 22 May 2018 • Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha, Jay Nanavati, Garrison Cottrell, Antonio Criminisi, Aditya Nori
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
Ranked #4 on
Brain Tumor Segmentation
on BRATS-2015
(using extra training data)
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).
1 code implementation • 28 Dec 2016 • Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker
In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.
no code implementations • 29 May 2016 • Yoad Lewenberg, Yoram Bachrach, Sukrit Shankar, Antonio Criminisi
We consider the task of predicting various traits of a person given an image of their face.
1 code implementation • NeurIPS 2016 • Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi
Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled.
no code implementations • CVPR 2017 • Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi
We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root.
no code implementations • CVPR 2016 • Sukrit Shankar, Duncan Robertson, Yani Ioannou, Antonio Criminisi, Roberto Cipolla
Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks.
1 code implementation • 3 Mar 2016 • Yani Ioannou, Duncan Robertson, Darko Zikic, Peter Kontschieder, Jamie Shotton, Matthew Brown, Antonio Criminisi
We present a systematic analysis of how to fuse conditional computation with representation learning and achieve a continuum of hybrid models with different ratios of accuracy vs. efficiency.
no code implementations • ICCV 2015 • Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulo
We present Deep Neural Decision Forests - a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner.
no code implementations • 20 Nov 2015 • Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, Antonio Criminisi
Applying our method to a near state-of-the-art network for CIFAR, we achieved comparable accuracy with 46% less compute and 55% fewer parameters.
no code implementations • CVPR 2014 • Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, Tim Paek
We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.
no code implementations • NeurIPS 2013 • Jamie Shotton, Toby Sharp, Pushmeet Kohli, Sebastian Nowozin, John Winn, Antonio Criminisi
Randomized decision trees and forests have a rich history in machine learning and have seen considerable success in application, perhaps particularly so for computer vision.
no code implementations • CVPR 2013 • Peter Kontschieder, Pushmeet Kohli, Jamie Shotton, Antonio Criminisi
This paper presents a new and efficient forest based model that achieves spatially consistent semantic image segmentation by encoding variable dependencies directly in the feature space the forests operate on.
no code implementations • CVPR 2013 • Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, Andrew Fitzgibbon
We address the problem of inferring the pose of an RGB-D camera relative to a known 3D scene, given only a single acquired image.
no code implementations • NeurIPS 2012 • Peter Kontschieder, Samuel R. Bulò, Antonio Criminisi, Pushmeet Kohli, Marcello Pelillo, Horst Bischof
In this paper we introduce Context-Sensitive Decision Forests - A new perspective to exploit contextual information in the popular decision forest framework for the object detection problem.