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isolated & continuous sign language recognition using CNN+LSTM/3D CNN/GCN/Encoder-Decoder

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isolated & continuous sign language recognition using CNN+LSTM/3D CNN/GCN/Encoder-Decoder

Requirements

Isolated Sign Language Recognition

CNN+LSTM

  1. four layers of Conv2d + one layer of LSTM

    Dataset Classes Samples Best Test Acc Best Test Loss
    CSL_Isolated 100 25,000 82.08% 0.734426
    CSL_Isolated 500 125,000 71.71% 1.332122
  2. ResNet + one layer of LSTM

    Dataset Classes Samples Best Test Acc Best Test Loss
    CSL_Isolated 100 25,000 93.54% 0.245582
    CSL_Isolated 500 125,000 83.17% 0.748759

3D CNN

  1. three layers of Conv3d

    Dataset Classes Samples Best Test Acc Best Test Loss
    CSL_Isolated 100 25,000 58.86% 1.560049
    CSL_Isolated 500 125,000 45.07% 2.255563
  2. 3D ResNet

    Method Dataset Classes Samples Best Test Acc Best Test Loss
    ResNet18 CSL_Isolated 100 25,000 93.30% 0.246169
    ResNet18 CSL_Isolated 500 125,000 79.42% 0.800490
    ResNet34 CSL_Isolated 100 25,000 94.78% 0.207592
    ResNet34 CSL_Isolated 500 125,000 81.61% 0.750424
    ResNet50 CSL_Isolated 100 25,000 94.36% 0.232631
    ResNet50 CSL_Isolated 500 125,000 83.15% 0.803212
    ResNet101 CSL_Isolated 100 25,000 95.26% 0.205430
    ResNet101 CSL_Isolated 500 125,000 83.18% 0.751727
  3. ResNet (2+1)D

    Dataset Classes Samples Best Test Acc Best Test Loss
    CSL_Isolated 100 25,000 98.68% 0.043099
    CSL_Isolated 500 125,000 94.85% 0.234880

GCN

Dataset Classes Samples Best Test Acc Best Test Loss
CSL_Skeleton 100 25,000 79.20% 0.737053
CSL_Skeleton 500 125,000 66.64% 1.165872

Skeleton+LSTM

Dataset Classes Samples Best Test Acc Best Test Loss
CSL_Skeleton 100 25,000 84.30% 0.488253
CSL_Skeleton 500 125,000 70.62% 1.078730

Continuous Sign Language Recognition

Encoder-Decoder

Encoder is ResNet18+LSTM, and Decoder is LSTM

Dataset Sentences Samples Best Test Wer Best Test Loss
CSL_Continuous 100 25,000 1.01% 0.034636
CSL_Continuous_Char 100 25,000 1.19% 0.049449

References

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  • Python 100.0%