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Sébastien Ehrhardt
Sébastien Ehrhardt
Verified email at robots.ox.ac.uk
Title
Cited by
Cited by
Year
Automatically discovering and learning new visual categories with ranking statistics
K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman
arXiv preprint arXiv:2002.05714, 2020
472020
Learning a physical long-term predictor
S Ehrhardt, A Monszpart, NJ Mitra, A Vedaldi
arXiv preprint arXiv:1703.00247, 2017
462017
Semi-supervised learning with scarce annotations
SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
362020
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
S Ehrhardt, O Groth, A Monszpart, M Engelcke, I Posner, N Mitra, ...
Advances in Neural Information Processing Systems 33, 2020
242020
Unsupervised intuitive physics from visual observations
S Ehrhardt, A Monszpart, N Mitra, A Vedaldi
Asian Conference on Computer Vision, 700-716, 2018
202018
Taking visual motion prediction to new heightfields
S Ehrhardt, A Monszpart, NJ Mitra, A Vedaldi
Computer Vision and Image Understanding 181, 14-25, 2019
192019
3d multi-bodies: Fitting sets of plausible 3d human models to ambiguous image data
B Biggs, D Novotny, S Ehrhardt, H Joo, B Graham, A Vedaldi
Advances in Neural Information Processing Systems 33, 20496-20507, 2020
172020
Co-attention for conditioned image matching
O Wiles, S Ehrhardt, A Zisserman
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
16*2021
Lsd-c: Linearly separable deep clusters
SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
142021
Small steps and giant leaps: Minimal newton solvers for deep learning
JF Henriques, S Ehrhardt, S Albanie, A Vedaldi
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
122019
Autonovel: Automatically discovering and learning novel visual categories
K Han, SA Rebuffi, S Ehrhardt, A Vedaldi, A Zisserman
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
102021
Learning to represent mechanics via long-term extrapolation and interpolation
S Ehrhardt, A Monszpart, A Vedaldi, N Mitra
arXiv preprint arXiv:1706.02179, 2017
82017
Stopping gan violence: Generative unadversarial networks
S Albanie, S Ehrhardt, JF Henriques
arXiv preprint arXiv:1703.02528, 2017
52017
Unsupervised intuitive physics from past experiences
S Ehrhardt, A Monszpart, NJ Mitra, A Vedaldi
arXiv preprint arXiv:1905.10793, 2019
42019
Deep Industrial Espionage
S Albanie, J Thewlis, S Ehrhardt, J Henriques
arXiv preprint arXiv:1904.01114, 2019
22019
Learning visual concepts with fewer human annotations
S Ehrhardt
University of Oxford, 2020
2020
LSD-C: Linearly Separable Deep Clusters–Supplementary Material–
SA Rebuffi, S Ehrhardt, K Han, A Vedaldi, A Zisserman
How does mini-batching affect curvature information for second order deep learning optimization?
D Granziol, X Wan, S Zohren, S Roberts, T Garipov, D Vetrov, AG Wilson, ...
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