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Hierarchical Co-occurrence Network with Prototype Loss for Few-shot Learning (PyTorch)

License: MIT License

Python 100.00%
3d-pose action-recognition few-shot few-shot-learning hcn hierarchical-co-occurrence-network meta-learning metric-learning nturgb-d one-shot-learning prototype-loss pytorch pytorch-implementation skeleton-based-action-recognition tensorboard

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hcn-prototypeloss-pytorch's Issues

Vanilla HCN accuracy

Thanks for the neat implementation.
I've run the provided code but couldn't get the reported accuracy, 88.12%. I got 81.1%.
Have you applied any other data post-processing for NTU-D, such as normalization, other than the code (interpolation, set hip to zero)?
Have you seen a result similar to mine by any chance from your previous experiments? If so, would you give me a hint to train the network properly?

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