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
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Posted Content
Fingerspelling Detection in American Sign Language
TL;DR: In this article, a multi-task learning model was proposed to detect fingerspelling in raw, untrimmed sign language videos, incorporating pose estimation and fingerspelling recognition along with detection.
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PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution
Pan Xie,Mengyi Zhao,Xiaohui Hu +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed a new model architecture, namely PiSLTRc, with two distinctive characteristics: (i) content-aware and position-aware convolution layers.
Book ChapterDOI
Sign Language Translation Using Multi Context Transformer.
M. Badri Narayanan,K. Mahesh Bharadwaj,G. R. Nithin,Dhiganth Rao Padamnoor,Vineeth Vijayaraghavan +4 more
TL;DR: Narayanan et al. as mentioned in this paper proposed a multi-context transformer to capture various temporal dependencies present between the frames to accurately translate the input signs and achieved state-of-the-art performance.
Proceedings ArticleDOI
Purification and Multi Temporal Semantic Network for Continuous Sign Language Recognition
TL;DR: Wang et al. as discussed by the authors proposed a purification and multi-temporal semantic network (PMTSNet) to handle the local consistency and context dependency problems in continuous sign language recognition.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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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.