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Tom Joy
Tom Joy
DPhil Student, Oxford University
Verified email at robots.ox.ac.uk - Homepage
Title
Cited by
Cited by
Year
Learning to adapt for stereo
A Tonioni, O Rahnama, T Joy, LD Stefano, T Ajanthan, PHS Torr
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
822019
Capturing label characteristics in vaes
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
arXiv preprint arXiv:2006.10102, 2020
392020
Learning multimodal VAEs through mutual supervision
T Joy, Y Shi, PHS Torr, T Rainforth, SM Schmon, N Siddharth
arXiv preprint arXiv:2106.12570, 2021
242021
Sample-dependent adaptive temperature scaling for improved calibration
T Joy, F Pinto, SN Lim, PHS Torr, PK Dokania
Proceedings of the AAAI Conference on Artificial Intelligence 37 (12), 14919 …, 2023
192023
Real-time highly accurate dense depth on a power budget using an fpga-cpu hybrid soc
O Rahnama, T Cavallari, S Golodetz, A Tonioni, T Joy, L Di Stefano, ...
IEEE Transactions on Circuits and Systems II: Express Briefs 66 (5), 773-777, 2019
192019
Rethinking semi-supervised learning in VAEs
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
arXiv preprint arXiv:2006.10102, 2020
132020
Efficient relaxations for dense crfs with sparse higher-order potentials
T Joy, A Desmaison, T Ajanthan, R Bunel, M Salzmann, P Kohli, PHS Torr, ...
SIAM journal on imaging sciences 12 (1), 287-318, 2019
122019
Impact of COVID‐19 on the management and outcomes of ureteric stones in the UK: a multicentre retrospective study
MHV Byrne, F Georgiades, A Light, CE Lovegrove, C Dominic, J Rahman, ...
BJU international 131 (1), 82-89, 2023
82023
Towards building self-aware object detectors via reliable uncertainty quantification and calibration
K Oksuz, T Joy, PK Dokania
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
32023
MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection
K Oksuz, S Kuzucu, T Joy, PK Dokania
arXiv preprint arXiv:2309.14976, 2023
2023
Variational autoencoders for supervision, calibration and multimodal learning
TW Joy
University of Oxford, 2022
2022
Supplementary material for Learning to Adapt for Stereo
A Tonioni, O Rahnama, T Joy, L Di Stefano, T Ajanthan, PHS Torr
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Articles 1–12