Stochastic Fine-Grained Labeling of Multi-state Sign Glosses for Continuous Sign Language Recognition
暂无分享,去创建一个
[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.