Fetching the paper…
Reading the bibliography…
In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted.
J. Forster, C. Schmidt, O. Koller, M. Bellgardt, and H. Ney, “Extensions of the sign language recognition and translation corpus rwth-phoenix-weather.” in LREC , 2014, pp. 1911–1916
1916
Earlier work this paper cites.
C. Feichtenhofer, A. Pinz, and A. Zisserman, “Convolutional two-stream network fusion for video action recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1933–1941
1941
Earlier work this paper cites.
E. T. Jaynes, “Information theory and statistical mechanics,” Physical review , vol. 106, no. 4, p. 620, 1957
1957
Earlier work this paper cites.
H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition,” IEEE transactions on acoustics, speech, and signal processing , vol. 26, no. 1, pp. 43–49, 1978
1978
Earlier work this paper cites.
C. Padden, “Verbs and role-shifting in american sign language,” in Proceedings of the fourth national symposium on sign language research and teaching , vol. 44. National Association of the Deaf Silver Spring, MD, 1986, p. 57
1986
Earlier work this paper cites.
M. W. Kadous et al. , “Machine recognition of auslan signs using powergloves: Towards large-lexicon recognition of sign language,” in Proceedings of the Workshop on the Integration of Gesture in Language and Speech , vol. 165, 1996
1996
Earlier work this paper cites.
T. K. Moon, “The expectation-maximization algorithm,” IEEE Signal processing magazine , vol. 13, no. 6, pp. 47–60, 1996
1996
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
J. Shawe-Taylor and N. Cristianini, “Support vector machines,” An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , pp. 93–112, 2000
2000
Earlier work this paper cites.
K. Emmorey, “Space on hand: The exploitation of signing space to illustrate abstract thought.” 2001
2001
Earlier work this paper cites.
W. Sandler and D. Lillo-Martin, Sign language and linguistic universals . Cambridge University Press, 2006
2006
Earlier work this paper cites.
R. E. Mitchell, T. A. Young, B. BACHELDA, and M. A. Karchmer, “How many people use asl in the united states? why estimates need updating,” Sign Language Studies , vol. 6, no. 3, pp. 306–335, 2006
2006
Earlier work this paper cites.
S. B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, and T. Darrell, “Hidden conditional random fields for gesture recognition,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) , vol. 2. IEEE, 2006, pp. 1521–1527
2006
Earlier work this paper cites.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in Proceedings of the 23rd international conference on Machine learning . ACM, 2006, pp. 369–376
2006
Earlier work this paper cites.
U. Von Agris, M. Knorr, and K.-F. Kraiss, “The significance of facial features for automatic sign language recognition,” in 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition . IEEE, 2008, pp. 1–6
2008
Earlier work this paper cites.
H. Cooper, B. Holt, and R. Bowden, “Sign language recognition,” in Visual Analysis of Humans . Springer, 2011, pp. 539–562
2011
Earlier work this paper cites.
H. Cooper, E.-J. Ong, N. Pugeault, and R. Bowden, “Sign language recognition using sub-units,” Journal of Machine Learning Research , vol. 13, no. Jul, pp. 2205–2231, 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
C. Wang, Z. Liu, and S.-C. Chan, “Superpixel-based hand gesture recognition with kinect depth camera,” IEEE transactions on multimedia , vol. 17, no. 1, pp. 29–39, 2014
2014
Earlier work this paper cites.
G. D. Evangelidis, G. Singh, and R. Horaud, “Continuous gesture recognition from articulated poses,” in European Conference on Computer Vision . Springer, 2014, pp. 595–607
2014
Earlier work this paper cites.
D. S. Alexiadis and P. Daras, “Quaternionic signal processing techniques for automatic evaluation of dance performances from mocap data,” IEEE Transactions on Multimedia , vol. 16, no. 5, pp. 1391–1406, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia . ACM, 2014, pp. 675–678
2014
Cited alongside, same era.
O. Koller, J. Forster, and H. Ney, “Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers,” Computer Vision and Image Understanding , vol. 141, pp. 108–125, 2015
2015
Cited alongside, same era.
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 2625–2634
2015
Cited alongside, same era.
P. Molchanov, S. Gupta, K. Kim, and J. Kautz, “Hand gesture recognition with 3d convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2015, pp. 1–7
2017
Later among the works it cites.
M. Cuturi and M. Blondel, “Soft-dtw: a differentiable loss function for time-series,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 894–903
2017
Later among the works it cites.
C. Wu, M. J. Gales, A. Ragni, P. Karanasou, and K. C. Sim, “Improving interpretability and regularization in deep learning,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 26, no. 2, pp. 256–265, 2017
2017
Later among the works it cites.
O. Koller, S. Zargaran, H. Ney, and R. Bowden, “Deep sign: Enabling robust statistical continuous sign language recognition via hybrid cnn-hmms,” International Journal of Computer Vision , vol. 126, no. 12, pp. 1311–1325, 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
N. Neverova, C. Wolf, G. Taylor, and F. Nebout, “Moddrop: adaptive multi-modal gesture recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38, no. 8, pp. 1692–1706, 2015
2015
Cited alongside, same era.
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri, “Learning spatiotemporal features with 3d convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 4489–4497
2015
Cited alongside, same era.
S. Tan, K. C. Sim, and M. Gales, “Improving the interpretability of deep neural networks with stimulated learning,” in 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) . IEEE, 2015, pp. 617–623
2015
Cited alongside, same era.
G. T. Papadopoulos and P. Daras, “Human action recognition using 3d reconstruction data,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 28, no. 8, pp. 1807–1823, 2016
2016
Cited alongside, same era.
F. Ronchetti, F. Quiroga, C. A. Estrebou, L. C. Lanzarini, and A. Rosete, “Lsa64: an argentinian sign language dataset,” in XXII Congreso Argentino de Ciencias de la Computación (CACIC 2016). , 2016
2016
Cited alongside, same era.
J. Zhang, W. Zhou, C. Xie, J. Pu, and H. Li, “Chinese sign language recognition with adaptive hmm,” in 2016 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2016, pp. 1–6
2016
Cited alongside, same era.
N. C. Camgoz, S. Hadfield, O. Koller, and R. Bowden, “Using convolutional 3d neural networks for user-independent continuous gesture recognition,” in 2016 23rd International Conference on Pattern Recognition (ICPR) . IEEE, 2016, pp. 49–54
2016
Cited alongside, same era.
D. S. Alexiadis, A. Chatzitofis, N. Zioulis, O. Zoidi, G. Louizis, D. Zarpalas, and P. Daras, “An integrated platform for live 3d human reconstruction and motion capturing,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 27, no. 4, pp. 798–813, 2016
2016
Cited alongside, same era.
D. Konstantinidis, K. Dimitropoulos, and P. Daras, “A deep learning approach for analyzing video and skeletal features in sign language recognition,” in 2018 IEEE International Conference on Imaging Systems and Techniques (IST) . IEEE, 2018, pp. 1–6
2018
Later among the works it cites.
H. Liu, S. Jin, and C. Zhang, “Connectionist temporal classification with maximum entropy regularization,” in Advances in Neural Information Processing Systems , 2018, pp. 831–841
2018
Later among the works it cites.
J. Huang, W. Zhou, Q. Zhang, H. Li, and W. Li, “Video-based sign language recognition without temporal segmentation,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
J. Pu, W. Zhou, and H. Li, “Dilated convolutional network with iterative optimization for continuous sign language recognition.” in IJCAI , vol. 3, 2018, p. 7
2018
Later among the works it cites.
N. Cihan Camgoz, S. Hadfield, O. Koller, H. Ney, and R. Bowden, “Neural sign language translation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7784–7793
2018
Later among the works it cites.
K. Hara, H. Kataoka, and Y. Satoh, “Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 6546–6555
2018
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
O. Koller, C. Camgoz, H. Ney, and R. Bowden, “Weakly supervised learning with multi-stream cnn-lstm-hmms to discover sequential parallelism in sign language videos,” IEEE transactions on pattern analysis and machine intelligence , 2019
2019
Later among the works it cites.
R. Cui, H. Liu, and C. Zhang, “A deep neural framework for continuous sign language recognition by iterative training,” IEEE Transactions on Multimedia , 2019
2019
Later among the works it cites.
H. Zhou, W. Zhou, and H. Li, “Dynamic pseudo label decoding for continuous sign language recognition,” in 2019 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2019, pp. 1282–1287
2019
Later among the works it cites.
J. Pu, W. Zhou, and H. Li, “Iterative alignment network for continuous sign language recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4165–4174
2019
Later among the works it cites.
J. Heymann, K. C. Sim, and B. Li, “Improving ctc using stimulated learning for sequence modeling,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5701–5705
2019
Later among the works it cites.
I. Papastratis, K. Dimitropoulos, D. Konstantinidis, and P. Daras, “Continuous sign language recognition through cross-modal alignment of video and text embeddings in a joint-latent space,” IEEE Access , vol. 8, pp. 91 170–91 180, 2020
2020
Closest in time.
D. Li, C. Rodriguez, X. Yu, and H. Li, “Word-level deep sign language recognition from video: A new large-scale dataset and methods comparison,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2020, pp. 1459–1469
2020
Closest in time.
N. C. Camgoz, O. Koller, S. Hadfield, and R. Bowden, “Sign language transformers: Joint end-to-end sign language recognition and translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 023–10 033
2020
Closest in time.