no code implementations • CVPR 2018 • Michael Firman, Neill D. F. Campbell, Lourdes Agapito, Gabriel J. Brostow
For a single input, we learn to predict a range of possible answers.
no code implementations • 16 Aug 2020 • Iaroslav Melekhov, Gabriel J. Brostow, Juho Kannala, Daniyar Turmukhambetov
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks.
1 code implementation • ECCV 2020 • Anita Rau, Guillermo Garcia-Hernando, Danail Stoyanov, Gabriel J. Brostow, Daniyar Turmukhambetov
Even when this is a known scene, the answer typically requires an expensive search across scale space, with matching and geometric verification of large sets of local features.
2 code implementations • ECCV 2020 • Jamie Watson, Oisin Mac Aodha, Daniyar Turmukhambetov, Gabriel J. Brostow, Michael Firman
We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs.
1 code implementation • CVPR 2020 • Jamie Watson, Michael Firman, Aron Monszpart, Gabriel J. Brostow
We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input.
1 code implementation • ICCV 2019 • Jamie Watson, Michael Firman, Gabriel J. Brostow, Daniyar Turmukhambetov
Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data.
Ranked #26 on Monocular Depth Estimation on KITTI Eigen split
2 code implementations • 20 Nov 2017 • Saki Shinoda, Daniel E. Worrall, Gabriel J. Brostow
Semi-supervised learning (SSL) partially circumvents the high cost of labeling data by augmenting a small labeled dataset with a large and relatively cheap unlabeled dataset drawn from the same distribution.
no code implementations • ICCV 2017 • Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
We propose a simple method to construct a deep feature space, with explicitly disentangled representations of several known transformations.
1 code implementation • CVPR 2017 • Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, Gabriel J. Brostow
This is not the case for rotations.
no code implementations • 26 Sep 2016 • Malcolm Reynolds, Tom S. F. Haines, Gabriel J. Brostow
We propose Swipe Mosaics, an interactive visualization that places the individual video frames on a 2D planar map that represents the layout of the physical scene.
16 code implementations • CVPR 2017 • Clément Godard, Oisin Mac Aodha, Gabriel J. Brostow
Learning based methods have shown very promising results for the task of depth estimation in single images.
Ranked #3 on Monocular Depth Estimation on Mid-Air Dataset
no code implementations • CVPR 2016 • Michael Firman, Oisin Mac Aodha, Simon Julier, Gabriel J. Brostow
Building a complete 3D model of a scene, given only a single depth image, is underconstrained.
no code implementations • CVPR 2014 • Oisin Mac Aodha, Neill D. F. Campbell, Jan Kautz, Gabriel J. Brostow
Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning.
no code implementations • CVPR 2015 • Edward Johns, Oisin Mac Aodha, Gabriel J. Brostow
However, image-importance is individual-specific, i. e. a teaching image is important to a student if it changes their overall ability to discriminate between classes.
no code implementations • 17 Feb 2015 • Thanapong Intharah, Gabriel J. Brostow
Accurate semantic labeling of image pixels is difficult because intra-class variability is often greater than inter-class variability.