no code implementations • 28 Nov 2022 • Yash Bhalgat, Joao F. Henriques, Andrew Zisserman
Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors.
no code implementations • 5 May 2021 • Dan Xu, Andrea Vedaldi, Joao F. Henriques
We build on the idea of view synthesis, which uses classical camera geometry to re-render a source image from a different point-of-view, specified by a predicted relative pose and depth map.
no code implementations • 31 Mar 2021 • Samuel Albanie, Erika Lu, Joao F. Henriques
In the quiet backwaters of cs. CV, cs. LG and stat. ML, a cornucopia of new learning systems is emerging from a primordial soup of mathematics-learning systems with no need for external supervision.
no code implementations • 28 Sep 2020 • Mandela Patrick, Yuki Asano, Polina Kuznetsova, Ruth Fong, Joao F. Henriques, Geoffrey Zweig, Andrea Vedaldi
In this paper, we show that, for videos, the answer is more complex, and that better results can be obtained by accounting for the interplay between invariance, distinctiveness, multiple modalities and time.
no code implementations • 31 Mar 2020 • Samuel Albanie, Jaime Thewmore, Robert McCraith, Joao F. Henriques
Peer review forms the backbone of modern scientific manuscript evaluation.
1 code implementation • ICCV 2019 • Joao F. Henriques, Sebastien Ehrhardt, Samuel Albanie, Andrea Vedaldi
We first validate our method, called CurveBall, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle.
1 code implementation • 1 Apr 2018 • Samuel Albanie, James Thewlis, Joao F. Henriques
Learning through experience is time-consuming, inefficient and often bad for your cortisol levels.
no code implementations • CVPR 2015 • Rui Caseiro, Joao F. Henriques, Pedro Martins, Jorge Batista
In this case, the source/target domains are represented in the form of subspaces, which are treated as points on the Grassmann manifold.
no code implementations • CVPR 2013 • Rui Caseiro, Pedro Martins, Joao F. Henriques, Fatima Silva Leite, Jorge Batista
In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [31, 27].