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Sameera Ramasinghe
Sameera Ramasinghe
Applied scientist, Amazon
Verified email at adelaide.edu.au
Title
Cited by
Cited by
Year
Gaussian activated neural radiance fields for high fidelity reconstruction and pose estimation
SF Chng, S Ramasinghe, J Sherrah, S Lucey
European Conference on Computer Vision, 264-280, 2022
812022
Beyond periodicity: Towards a unifying framework for activations in coordinate-mlps
S Ramasinghe, S Lucey
European Conference on Computer Vision, 142-158, 2022
762022
Synthesized feature based few-shot class-incremental learning on a mixture of subspaces
A Cheraghian, S Rahman, S Ramasinghe, P Fang, C Simon, L Petersson, ...
Proceedings of the IEEE/CVF international conference on computer vision …, 2021
622021
Rethinking positional encoding
J Zheng, S Ramasinghe, S Lucey
arXiv preprint arXiv:2107.02561, 2021
432021
Spectral-gans for high-resolution 3d point-cloud generation
S Ramasinghe, S Khan, N Barnes, S Gould
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020
352020
Enabling equivariance for arbitrary lie groups
LE MacDonald, S Ramasinghe, S Lucey
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
232022
Action recognition by single stream convolutional neural networks: An approach using combined motion and static information
S Ramasinghe, R Rodrigo
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 101-105, 2015
212015
Recognition of badminton strokes using dense trajectories
S Ramasinghe, KGM Chathuramali, R Rodrigo
7th International Conference on Information and Automation for …, 2014
192014
Trading positional complexity vs deepness in coordinate networks
J Zheng, S Ramasinghe, X Li, S Lucey
European Conference on Computer Vision, 144-160, 2022
162022
A context-aware capsule network for multi-label classification
S Ramasinghe, CD Athuraliya, SH Khan
Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 0-0, 2018
162018
Combined static and motion features for deep-networks-based activity recognition in videos
S Ramasinghe, J Rajasegaran, V Jayasundara, K Ranasinghe, ...
IEEE Transactions on Circuits and Systems for Video Technology 29 (9), 2693-2707, 2017
152017
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds
S Ramasinghe, M Farazi, SH Khan, N Barnes, S Gould
Advances in Neural Information Processing Systems 34, 19487-19499, 2021
112021
On the frequency-bias of coordinate-mlps
S Ramasinghe, LE MacDonald, S Lucey
Advances in Neural Information Processing Systems 35, 796-809, 2022
102022
Robust normalizing flows using Bernstein-type polynomials
S Ramasinghe, K Fernando, S Khan, N Barnes
arXiv preprint arXiv:2102.03509, 2021
102021
Representation learning on unit ball with 3d roto-translational equivariance
S Ramasinghe, S Khan, N Barnes, S Gould
International Journal of Computer Vision 128 (6), 1612-1634, 2020
102020
Few-shot class-incremental learning for 3d point cloud objects
T Chowdhury, A Cheraghian, S Ramasinghe, S Ahmadi, M Saberi, ...
European Conference on Computer Vision, 204-220, 2022
92022
Learning positional embeddings for coordinate-mlps
S Ramasinghe, S Lucey
arXiv preprint arXiv:2112.11577, 2021
82021
Conditional Generative Modeling via Learning the Latent Space
S Ramasinghe, K Ranasinghe, S Khan, N Barnes, S Gould
International Conference on Learning Representations. 2020., 2020
82020
Spectral-gans for high-resolution 3d point-cloud generation. In 2020 IEEE
S Ramasinghe, S Khan, N Barnes, S Gould
RSJ International Conference on Intelligent Robots and Systems (IROS), 8169-8176, 0
8
BLiRF: Bandlimited Radiance Fields for Dynamic Scene Modeling
S Ramasinghe, V Shevchenko, G Avraham, A Van Den Hengel
Proceedings of the AAAI Conference on Artificial Intelligence 38 (5), 4641-4649, 2024
62024
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