| 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 | 14 | 2015 |
| 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 | 8 | 2020 |
| 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 | 8 | 2017 |
| 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 | 7 | 2018 |
| Recognition of badminton strokes using dense trajectories S Ramasinghe, KGM Chathuramali, R Rodrigo 7th International Conference on Information and Automation for …, 2014 | 7 | 2014 |
| 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 | 6 | 2020 |
| Abnormal activity recognition using spatio-temporal features KGM Chathuramali, S Ramasinghe, R Rodrigo 7th International Conference on Information and Automation for …, 2014 | 6 | 2014 |
| Blended convolution and synthesis for efficient discrimination of 3d shapes S Ramasinghe, S Khan, N Barnes, S Gould Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 4 | 2020 |
| Volumetric convolution: Automatic representation learning in unit ball S Ramasinghe, S Khan, N Barnes arXiv preprint arXiv:1901.00616, 2019 | 4 | 2019 |
| Robust normalizing flows using Bernstein-type polynomials S Ramasinghe, K Fernando, S Khan, N Barnes arXiv preprint arXiv:2102.03509, 2021 | 2 | 2021 |
| Conditional Generative Modeling via Learning the Latent Space S Ramasinghe, K Ranasinghe, S Khan, N Barnes, S Gould arXiv preprint arXiv:2010.03132, 2020 | 1 | 2020 |
| Micro Actions and Deep Static Features for Activity Recognition S Ramasinghe, J Rajasegaran, V Jayasundara, K Ranasinghe, ... 2017 International Conference on Digital Image Computing: Techniques and …, 2017 | 1 | 2017 |
| Rethinking positional encoding J Zheng, S Ramasinghe, S Lucey arXiv preprint arXiv:2107.02561, 2021 | | 2021 |
| 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 | | 2021 |
| How to train your conditional GAN: An approach using geometrically structured latent manifolds S Ramasinghe, M Farazi, S Khan, N Barnes, S Gould arXiv preprint arXiv:2011.13055, 2020 | | 2020 |
| ICPRAM 2020-PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION APPLICATIONS AND METHODS J Jeong, S Lee, N Kwak, S Ramasinghe, S Khan, N Barnes, G Ardon, ... | | 2020 |
| Learned and Hand-crafted Feature Fusion in Unit Ball for 3D Object Classification. S Ramasinghe, SH Khan, N Barnes ICPRAM, 115-125, 2020 | | 2020 |
| Annex 17: deep semantic segmentation for built-up area extraction and mapping from satellite imagery CD Athuraliya, S Ramasinghe, S Lokanathan | | 2018 |
| Activity recognition combined with scene context and action sequence SC Ramasinghe | | 2017 |
| Poster: Advanced Feature Based Deep Learning for Intelligent Human Activity Recognition: An Approach using Scene Context and Composition of Sub Events. S Ramasinghe Proceedings of the 14th Annual International Conference on Mobile Systems …, 2016 | | 2016 |
Salman KhanMBZUAI, Australian National UniversityVerified email at anu.edu.au
