Stochastic Fine-Grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition

[1]  Hermann Ney,et al.  Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Oscar Koller,et al.  Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Houqiang Li,et al.  Continuous Sign Language Recognition via Reinforcement Learning , 2019, 2019 IEEE International Conference on Image Processing (ICIP).

[4]  Zhaoyang Yang,et al.  SF-Net: Structured Feature Network for Continuous Sign Language Recognition , 2019, ArXiv.

[5]  Houqiang Li,et al.  Dynamic Pseudo Label Decoding for Continuous Sign Language Recognition , 2019, 2019 IEEE International Conference on Multimedia and Expo (ICME).

[6]  Houqiang Li,et al.  Iterative Alignment Network for Continuous Sign Language Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Jan Niehues,et al.  Very Deep Self-Attention Networks for End-to-End Speech Recognition , 2019, INTERSPEECH.

[8]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[9]  Xiaojuan Qi,et al.  Self-boosted Gesture Interactive System with ST-Net , 2018, ACM Multimedia.

[10]  Hermann Ney,et al.  Neural Sign Language Translation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[11]  Ashish Vaswani,et al.  Self-Attention with Relative Position Representations , 2018, NAACL.

[12]  Jie Huang,et al.  Video-based Sign Language Recognition without Temporal Segmentation , 2018, AAAI.

[13]  Oscar Koller,et al.  SubUNets: End-to-End Hand Shape and Continuous Sign Language Recognition , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[14]  Hermann Ney,et al.  Re-Sign: Re-Aligned End-to-End Sequence Modelling with Deep Recurrent CNN-HMMs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Changshui Zhang,et al.  Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[17]  Hermann Ney,et al.  Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition , 2016, BMVC.

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Hermann Ney,et al.  Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers , 2015, Comput. Vis. Image Underst..

[20]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[21]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[22]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[24]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[25]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[26]  Daniel Jurafsky,et al.  First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs , 2014, ArXiv.

[27]  Ronald J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.