|
--- |
|
dataset_info: |
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- config_name: multisigner |
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features: |
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- name: tokens |
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sequence: string |
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- name: frames |
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sequence: image |
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splits: |
|
- name: train |
|
num_bytes: 35090699166 |
|
num_examples: 5672 |
|
- name: validation |
|
num_bytes: 3294861848 |
|
num_examples: 540 |
|
- name: test |
|
num_bytes: 3935889563 |
|
num_examples: 629 |
|
download_size: 43042303939 |
|
dataset_size: 42321450577 |
|
- config_name: signerindependent |
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features: |
|
- name: tokens |
|
sequence: string |
|
- name: frames |
|
sequence: image |
|
splits: |
|
- name: train |
|
num_bytes: 26933878872 |
|
num_examples: 4376 |
|
- name: validation |
|
num_bytes: 720567494 |
|
num_examples: 111 |
|
- name: test |
|
num_bytes: 1175795394 |
|
num_examples: 180 |
|
download_size: 29320607031 |
|
dataset_size: 28830241760 |
|
--- |
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# RWTH-Weather-Phoenix 2014 |
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This archive contains two sets of the RWTH-Weather-Phoenix 2014 corpus |
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1. the multisigner set |
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2. the signer independent set. |
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It is released under non-commercial cc 4.0 license with attribution (see attachment) |
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If you use this data in your research, please cite: |
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``` |
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O. Koller, J. Forster, and H. Ney. Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers. Computer Vision and Image Understanding, volume 141, pages 108-125, December 2015. |
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``` |
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and |
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``` |
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Koller, Zargaran, Ney. "Re-Sign: Re-Aligned End-to-End Sequence Modeling with Deep Recurrent CNN-HMMs" in CVPR 2017, Honululu, Hawaii, USA. |
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``` |
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See README files in subfolders for more information. |
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### CHANGELOG |
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- v1 Aug 20 2016, initial version of the archive. multisigner setup |
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- v2 Apr 21 2017, signer independent SI5 subset, added caffe models and automatic frame-alignment |
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- v3 Nov 3 2017, added language models and complete set of hyper parameters to reproduce the published results |
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