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The Sign Language Production (SLP) project aims to automatically translate spoken languages into sign sequences.
Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives
Saunders, B.; Camgöz, N. C.; and Bowden, R. 2021 · 1909
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A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, R. J.; and Zipser, D. 1989 · 1989
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Bleu: a Method for Automatic Evaluation of Machine Translation
Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002
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Progressive Transformers for End-to-End Sign Language Production
Saunders, B.; Camgöz, N. C.; and Bowden, R. 2020 · 2004
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Visualizing data using t-SNE
Van der Maaten, L.; and Hinton, G. 2008 · 2008
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Multi-channel Transformers for Multi-articulatory Sign Language Translation
Camgöz, N. C.; Koller, O.; Hadfield, S.; and Bowden, R. 2020 · 2009
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DGS Corpus & Dicta-Sign: The Hamburg Studio Setup
Hanke, T.; König, L.; Wagner, S.; and Matthes, S. 2010 · 2010
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Everybody Sign Now: Translating Spoken Language to Photo Realistic Sign Language Video
Saunders, B.; Camgoz, N. C.; and Bowden, R. 2020 · 2011
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J.; Weiss, E. A.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
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Ba, J.; Kiros, J. R.; and Hinton, G. E. 2016 · 2016
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Study on density peaks clustering based on k-nearest neighbors and principal component analysis
Du, M.; Ding, S.; and Jia, H. 2016 · 2016
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Neural Discrete Representation Learning
van den Oord, A.; Vinyals, O.; and Kavukcuoglu, K. 2017 · 2017
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Neural Sign Language Translation
Camgöz, N. C.; Hadfield, S.; Koller, O.; Ney, H.; and Bowden, R. 2018 · 2018
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Non-Autoregressive Neural Machine Translation
Gu, J.; Bradbury, J.; Xiong, C.; Li, V. O. K.; and Socher, R. 2018 · 2018
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The syntax of sign language agreement: Common ingredients, but unusual recipe
Pfau, R.; Salzmann, M.; and Steinbach, M. 2018 · 2018
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Sign Language Production using Neural Machine Translation and Generative Adversarial Networks
Stoll, S.; Camgöz, N. C.; Hadfield, S.; and Bowden, R. 2018 · 2018
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Structured Prediction Helps 3D Human Motion Modelling
Aksan, E.; Kaufmann, M.; and Hilliges, O. 2019 · 2019
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Everybody Dance Now
Chan, C.; Ginosar, S.; Zhou, T.; and Efros, A. A. 2019 · 2019
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Mask-Predict: Parallel Decoding of Conditional Masked Language Models
Ghazvininejad, M.; Levy, O.; Liu, Y.; and Zettlemoyer, L. 2019 · 2019
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Generalization in Generation: A closer look at Exposure Bias
Schmidt, F. 2019 · 2019
Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
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Taming Transformers for High-Resolution Image Synthesis
Esser, P.; Rombach, R.; and Ommer, B. 2021 · 2021
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Vector Quantized Diffusion Model for Text-to-Image Synthesis
Gu, S.; Chen, D.; Bao, J.; Wen, F.; Zhang, B.; Chen, D.; Yuan, L.; and Guo, B. 2021 · 2021
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Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models
Hoogeboom, E.; Nielsen, D.; Jaini, P.; Forr’e, P.; and Welling, M. 2021 · 2021
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SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition
Hu, H.; Zhao, W.; gang Zhou, W.; Wang, Y.; and Li, H. 2021 · 2021
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Cited alongside, same era.
Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation
Camgöz, N. C.; Koller, O.; Hadfield, S.; and Bowden, R. 2020 · 2020
Cited alongside, same era.
Denoising Diffusion Probabilistic Models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Cited alongside, same era.
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
Kim, J.; Kim, S.; Kong, J.; and Yoon, S. 2020 · 2020
Cited alongside, same era.
Adversarial Training for Multi-Channel Sign Language Production
Saunders, B.; Bowden, R.; and Camgöz, N. C. 2020 · 2020
Cited alongside, same era.
Skeleton-based Chinese sign language recognition and generation for bidirectional communication between deaf and hearing people
Xiao, Q.; Qin, M.; and Yin, Y. 2020 · 2020
Cited alongside, same era.
Neural Sign Language Synthesis: Words Are Our Glosses
Zelinka, J.; and Kanis, J. 2020 · 2020
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Towards Fast and High-Quality Sign Language Production
Huang, W.; Pan, W.; Zhao, Z.; and Tian, Q. 2021 · 2021
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Hierarchical Transformers Are More Efficient Language Models
Nawrot, P.; Tworkowski, S.; Tyrolski, M.; Kaiser, L.; Wu, Y.; Szegedy, C.; and Michalewski, H. 2021 · 2021
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Improved Denoising Diffusion Probabilistic Models
Nichol, A. Q.; and Dhariwal, P. 2021 · 2021
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High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2021 · 2021
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Score-Based Generative Modeling through Stochastic Differential Equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2021 · 2021
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PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution
Xie, P.; Zhao, M.; and Hu, X. 2021 · 2021
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Cascaded Diffusion Models for High Fidelity Image Generation
Ho, J.; Saharia, C.; Chan, W.; Fleet, D.; Norouzi, M.; and Salimans, T. 2022 · 2022
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Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production
Saunders, B.; Camgoz, N. C.; and Bowden, R. 2022 · 2022
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Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer
Zeng, W.; Jin, S.; Liu, W.; Qian, C.; Luo, P.; Wanli, O.; and Wang, X. 2022 · 2022
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Spatial-Temporal Multi-Cue Network for Sign Language Recognition and Translation
Zhou, H.; gang Zhou, W.; Zhou, Y.; and Li, H. 2022 · 2022
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