Brief Introduction to Continuous Sign Language Recognition - - PowerPoint PPT Presentation

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Brief Introduction to Continuous Sign Language Recognition - - PowerPoint PPT Presentation

Brief Introduction to Continuous Sign Language Recognition 2019.1.19 Introduction What does a continuous sign language recognition (SLR) system do? word vocabulary: apple, sun, today, catch, you today is SLR system


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Brief Introduction to Continuous Sign Language Recognition

魏承承 2019.1.19

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Introduction

 What does a continuous sign language recognition (SLR) system do?

word vocabulary: apple, sun, today, catch, you ……

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today is sunny … SLR system sign video sentence

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Introduction

 Evaluation on Continuous SLR

 Word Error Rate (WER) For example, prediction: I (have) a cat that named Jerry. groundtruth: I have a cat named Tom. Calculate the WER:

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1 1 1=0.5 6  

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Introduction

 Continuous SLR is weakly-supervised  解决 Continuous SLR 问题的主流思路

 受语音识别领域启发:对每一帧识别,合并结果

 Connectionist Temporal Classification ( CTC )  CNN-RNN-CTC framework

 受机器翻译领域启发:从特征序列映射到文本序列

 Encoder-Decoder framework

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Introduction

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 CTC: 逐一识别,再合并

Graves A, Fernández S, Gomez F, et al. Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. ICML 2006

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Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization [CVPR 2017]

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Framework : Spatio-temporal CNN - BLSTM - CTC

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Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization [CVPR 2017]

 Step1: end-to-end learning

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Conv1D: 沿时间维度卷积 (K+1)×N d×N

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Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization [CVPR 2017]

 Step2: Feature learning with alignment proposal

 alignment proposal: output of BLSTM  to finetune the spatio-temporal feature extractor

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Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization [CVPR 2017]

 Step3: Sequence learning from representations

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Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization [CVPR 2017]

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 Experimental results

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Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization [CVPR 2017]

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 Comparisons

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Recurrent Convolutional Neural Networks for Continuous Sign Language Recognition by Staged Optimization [CVPR 2017]

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 Motivated by this paper…

 alignment proposal: probability distribution -> argmax-> word  a staged optimization -> more staged optimization  ……

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Connectionist Temporal Fusion for Sign Language Translation [MM2019]

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 Temporal COV

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Connectionist Temporal Fusion for Sign Language Translation [MM2019]

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 Optimization  Decoding

 argmax-> delete blank -> delete continuous repetitions

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Connectionist Temporal Fusion for Sign Language Translation [MM2019]

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 experimental result

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Connectionist Temporal Fusion for Sign Language Translation [MM2019]

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 experimental result

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Connectionist Temporal Fusion for Sign Language Translation [MM2019]

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 Comparisons

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Connectionist Temporal Fusion for Sign Language Translation [MM2019]

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The end Thank you