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# Zero Shot Action Recognition
This is the git repo of the paper **Data Augmentation of Semantic Embeddings for Skeleton based Zero-Shot
Gesture Recognition** by David Heiming, Hannes Uhl and Jonas Linkerhaegner from the summer term 2021. Following our modular approach in the following sections you can read how to use our code.
# ST-GCN
This module is located in the folder **st-gcn_original** and can be used like the original from the git repo [st-gcn](https://github.com/yysijie/st-gcn). Additional files:
- **/config/st_gcn** contains two new folders with the config files for our splits
- **/tools** contains three python scripts "ntu_gendata.py", "ntu_gendata_zsasr.py" and "ntu_gendata_zsar_nearest_cos.py" to generate the training splits for the ST-GCN.
- **/processor** contains the python file "feature_extraction.py" to generate the 256 dimensional features of the the classes the ST-GCN was not trained on
**SBERT**
This module is located in the folder **Bert_language_embeddings**. The most important file to generate the SBERT mean class label embedding is the python file "class_label_embedding.py".
**Relation Net**
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