Követés
Sadeep Jayasumana
Sadeep Jayasumana
Research Scientist, Google Research
E-mail megerősítve itt: google.com - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
Conditional Random Fields as Recurrent Neural Networks
S Zheng, S Jayasumana, B Romera-Paredes, V Vineet, Z Su, D Du, ...
IEEE International Conference on Computer Vision (ICCV), 2015
32142015
Long-Tail Learning via Logit Adjustment
AK Menon, S Jayasumana, AS Rawat, H Jain, A Veit, S Kumar
ICLR, 2021
5302021
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
S Jayasumana, R Hartley, M Salzmann, H Li, M Harandi
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, 73-80, 2013
3522013
Higher order conditional random fields in deep neural networks
A Arnab, S Jayasumana, S Zheng, PHS Torr
Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016
2812016
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
S Jayasumana, R Hartley, M Salzmann, H Li, M Harandi
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2015
2502015
Conditional random fields meet deep neural networks for semantic segmentation: Combining probabilistic graphical models with deep learning for structured prediction
A Arnab, S Zheng, S Jayasumana, B Romera-Paredes, M Larsson, ...
IEEE Signal Processing Magazine 35 (1), 37-52, 2018
1622018
Expanding the family of grassmannian kernels: An embedding perspective
MT Harandi, M Salzmann, S Jayasumana, R Hartley, H Li
Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland …, 2014
1092014
A framework for shape analysis via hilbert space embedding
S Jayasumana, M Salzmann, H Li, M Harandi
Proceedings of the IEEE International Conference on Computer Vision, 1249-1256, 2013
442013
Optimizing Over Radial Kernels on Compact Manifolds
S Jayasumana, R Hartley, M Salzmann, H Li, M Harandi
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014
422014
Prototypical Priors: From Improving Classification to Zero-Shot Learning
S Jetley, B Romera-Paredes, S Jayasumana, P Torr
The British Machine Vision Conference (BMVC), 2015
412015
Higher order potentials in end-to-end trainable conditional random fields
A Arnab, S Jayasumana, S Zheng, PHS Torr
arXiv preprint arXiv:1511.08119 2, 2015
332015
Combining multiple manifold-valued descriptors for improved object recognition
S Jayasumana, R Hartley, M Salzmann, H Li, M Harandi
2013 International Conference on Digital Image Computing: Techniques and …, 2013
302013
In defense of dual-encoders for neural ranking
A Menon, S Jayasumana, AS Rawat, S Kim, S Reddi, S Kumar
International Conference on Machine Learning, 15376-15400, 2022
172022
Disentangling sampling and labeling bias for learning in large-output spaces
AS Rawat, AK Menon, W Jitkrittum, S Jayasumana, F Yu, S Reddi, ...
International Conference on Machine Learning, 8890-8901, 2021
92021
Kernelized classification in deep networks
S Jayasumana, S Ramalingam, S Kumar
arXiv preprint arXiv:2012.09607, 2020
92020
Bipartite Conditional Random Fields for Panoptic Segmentation
S Jayasumana, K Ranasinghe, M Jayawardhana, S Liyanaarachchi, ...
British Machine Vision Conference (BMVC), 2020
92020
Kernels on Riemannian manifolds
S Jayasumana, R Hartley, M Salzmann
Riemannian computing in computer vision, 45-67, 2016
82016
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval
S Kim, AS Rawat, M Zaheer, S Jayasumana, V Sadhanala, W Jitkrittum, ...
arXiv preprint arXiv:2301.12005, 2023
42023
Do We Need Neural Collapse? Learning Diverse Features for Fine-grained and Long-tail Classification
J Ma, C You, SJ Reddi, S Jayasumana, H Jain, F Yu, SF Chang, S Kumar
32022
Less is more: Selecting informative and diverse subsets with balancing constraints
S Ramalingam, D Glasner, K Patel, R Vemulapalli, S Jayasumana, ...
arXiv preprint arXiv:2104.12835, 2021
22021
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