Proceedings ArticleDOI
Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization
Runpeng Cui,Hu Liu,Changshui Zhang +2 more
- pp 1610-1618
TLDR
This work presents a weakly supervised framework with deep neural networks for vision-based continuous sign language recognition, where the ordered gloss labels but no exact temporal locations are available with the video of sign sentence, and the amount of labeled sentences for training is limited.Abstract:
This work presents a weakly supervised framework with deep neural networks for vision-based continuous sign language recognition, where the ordered gloss labels but no exact temporal locations are available with the video of sign sentence, and the amount of labeled sentences for training is limited. Our approach addresses the mapping of video segments to glosses by introducing recurrent convolutional neural network for spatio-temporal feature extraction and sequence learning. We design a three-stage optimization process for our architecture. First, we develop an end-to-end sequence learning scheme and employ connectionist temporal classification (CTC) as the objective function for alignment proposal. Second, we take the alignment proposal as stronger supervision to tune our feature extractor. Finally, we optimize the sequence learning model with the improved feature representations, and design a weakly supervised detection network for regularization. We apply the proposed approach to a real-world continuous sign language recognition benchmark, and our method, with no extra supervision, achieves results comparable to the state-of-the-art.read more
Citations
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Proceedings ArticleDOI
Neural Sign Language Translation
TL;DR: This work formalizes SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge) and allows to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language.
Journal ArticleDOI
A Deep Neural Framework for Continuous Sign Language Recognition by Iterative Training
TL;DR: This work develops a continuous sign language (SL) recognition framework with deep neural networks, which directly transcribes videos of SL sentences to sequences of ordered gloss labels, and proposed architecture adopts deep convolutional neural networks with stacked temporal fusion layers as the feature extraction module.
Journal ArticleDOI
Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos
TL;DR: This work applies the approach to the domain of sign language recognition exploiting the sequential parallelism to learn sign language, mouth shape and hand shape classifiers and clearly outperform the state-of-the-art on all data sets and observe significantly faster convergence using the parallel alignment approach.
Posted Content
Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation
TL;DR: A novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation while being trainable in an end-to-end manner is introduced by using a Connectionist Temporal Classification (CTC) loss to bind the recognition and translation problems into a single unified architecture.
Proceedings ArticleDOI
Iterative Alignment Network for Continuous Sign Language Recognition
TL;DR: The framework consists of a 3D convolutional residual network for feature learning and an encoder-decoder network with connectionist temporal classification (CTC) for sequence modelling that is optimized in an alternate way for weakly supervised continuous sign language recognition.
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