Proceedings ArticleDOI
Re-Sign: Re-Aligned End-to-End Sequence Modelling with Deep Recurrent CNN-HMMs
Oscar Koller,Sepehr Zargaran,Hermann Ney +2 more
- pp 3416-3424
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TLDR
This work proposes an algorithm that treats the provided training labels as weak labels and refines the label-to-image alignment on-the-fly in a weakly supervised fashion, and embedded into an HMM the resulting deep model continuously improves its performance in several re-alignments.Abstract:
This work presents an iterative re-alignment approach applicable to visual sequence labelling tasks such as gesture recognition, activity recognition and continuous sign language recognition. Previous methods dealing with video data usually rely on given frame labels to train their classifiers. However, looking at recent data sets, these labels often tend to be noisy which is commonly overseen. We propose an algorithm that treats the provided training labels as weak labels and refines the label-to-image alignment on-the-fly in a weakly supervised fashion. Given a series of frames and sequence-level labels, a deep recurrent CNN-BLSTM network is trained end-to-end. Embedded into an HMM the resulting deep model corrects the frame labels and continuously improves its performance in several re-alignments. We evaluate on two challenging publicly available sign recognition benchmark data sets featuring over 1000 classes. We outperform the state-of-the-art by up to 10% absolute and 30% relative.read more
Citations
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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.
Proceedings Article
Hierarchical LSTM for Sign Language Translation
TL;DR: A hierarchical-LSTM (HLSTM) encoderdecoder model with visual content and word embedding for SLT exhibits promising performance on singer-independent test with seen sentences and also outperforms the comparison algorithms on unseen sentences.
Proceedings ArticleDOI
Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation
TL;DR: Sign Language Transformers as mentioned in this paper use a Connectionist Temporal Classification (CTC) loss to bind the recognition and translation problems into a single unified architecture, which leads to significant performance gains.
Journal ArticleDOI
Deep Sign: Enabling Robust Statistical Continuous Sign Language Recognition via Hybrid CNN-HMMs
TL;DR: This manuscript introduces the end-to-end embedding of a CNN into a HMM, while interpreting the outputs of the CNN in a Bayesian framework, and compares the hybrid modelling to a tandem approach and evaluates the gain of model combination.
Journal ArticleDOI
Spatial-Temporal Multi-Cue Network for Continuous Sign Language Recognition
TL;DR: Wang et al. as discussed by the authors proposed a spatial-temporal multi-cue (STMC) network to solve the vision-based sequence learning problem and achieved state-of-the-art performance on three large-scale CSLR benchmarks.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.