| Neural sign language translation N Cihan Camgoz, S Hadfield, O Koller, H Ney, R Bowden Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 713 | 2018 |
| Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation NC Camgoz, O Koller, S Hadfield, R Bowden Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 594 | 2020 |
| Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers O Koller, J Forster, H Ney Computer Vision and Image Understanding 141, 108-125, 2015 | 517 | 2015 |
| Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective D Bragg, O Koller, M Bellard, L Berke, P Boudreault, A Braffort, N Caselli, ... The 21st International ACM SIGACCESS Conference on Computers and …, 2019 | 433 | 2019 |
| Subunets: End-to-end hand shape and continuous sign language recognition N Cihan Camgoz, S Hadfield, O Koller, R Bowden Proceedings of the IEEE International Conference on Computer Vision, 3056-3065, 2017 | 377 | 2017 |
| Deep hand: How to train a CNN on 1 million hand images when your data is continuous and weakly labelled O Koller, H Ney, R Bowden Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 355 | 2016 |
| Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos O Koller, C Camgoz, H Ney, R Bowden IEEE transactions on pattern analysis and machine intelligence, 2019 | 337 | 2019 |
| Re-sign: Re-aligned end-to-end sequence modelling with deep recurrent CNN-HMMs O Koller, S Zargaran, H Ney Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 299 | 2017 |
| MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language HRV Joze, O Koller arXiv preprint arXiv:1812.01053, 2018 | 291 | 2018 |
| Deep sign: Hybrid CNN-HMM for continuous sign language recognition O Koller, O Zargaran, H Ney, R Bowden Proceedings of the British Machine Vision Conference 2016, 2016 | 285 | 2016 |
| RWTH-PHOENIX-Weather: A Large Vocabulary Sign Language Recognition and Translation Corpus. J Forster, C Schmidt, T Hoyoux, O Koller, U Zelle, JH Piater, H Ney LREC, 3785-3789, 2012 | 214 | 2012 |
| Deep Sign: Enabling Robust Statistical Continuous Sign Language Recognition via Hybrid CNN-HMMs O Koller, S Zargaran, H Ney, R Bowden International Journal of Computer Vision 126 (12), 1311-1325, 2018 | 200 | 2018 |
| Extensions of the Sign Language Recognition and Translation Corpus RWTH-PHOENIX-Weather. J Forster, C Schmidt, O Koller, M Bellgardt, H Ney LREC, 1911-1916, 2014 | 198 | 2014 |
| Multi-channel transformers for multi-articulatory sign language translation NC Camgoz, O Koller, S Hadfield, R Bowden European Conference on Computer Vision, 301-319, 2020 | 158 | 2020 |
| Deep learning of mouth shapes for sign language O Koller, H Ney, R Bowden Proceedings of the IEEE International Conference on Computer Vision …, 2015 | 155 | 2015 |
| Using convolutional 3d neural networks for user-independent continuous gesture recognition NC Camgoz, S Hadfield, O Koller, R Bowden 2016 23rd International Conference on Pattern Recognition (ICPR), 49-54, 2016 | 114 | 2016 |
| Quantitative Survey of the State of the Art in Sign Language Recognition O Koller arXiv preprint arXiv:2008.09918, 2020 | 110 | 2020 |
| The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective D Bragg, N Caselli, JA Hochgesang, M Huenerfauth, L Katz-Hernandez, ... ACM Transactions on Accessible Computing (TACCESS) 14 (2), 1-45, 2021 | 53 | 2021 |
| Sign spotting using hierarchical sequential patterns with temporal intervals EJ Ong, O Koller, N Pugeault, R Bowden Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2014 | 52 | 2014 |
| Automatic alignment of hamnosys subunits for continuous sign language recognition O Koller, R Bowden, H Ney LREC 2016 Proceedings, 121-128, 2016 | 49 | 2016 |
Hermann NeyRWTH Aachen University在 cs.rwth-aachen.de 的電子郵件地址已通過驗證
