Comments (6)
DragonLiu1995
commented on July 1, 2024
For Cross-View, we used n_neighbours=1, but for Cross-Subject we actually tried a set of parameters(1, 3, 5, 7, 9). This code snippet was cleaned up from the jupyter notebook we did for Cross-Subject experiment.
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ChengYiBin
commented on July 1, 2024
So for Cross-Subject, k=9 is the best hyper-parameter for action recognition, and is selected for all Cross-Subject evaluation, right ?
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DragonLiu1995
commented on July 1, 2024
Yes.
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ChengYiBin
commented on July 1, 2024
sorry to bother again. About the experimental results in this paper, those unsupervised method compared with this work, I wonder whether you re-implement them according to original papers (i.e. fine-tune a final FC layer), or also by KNN classification?
thanks!
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DragonLiu1995
commented on July 1, 2024
Except for LongT GAN which is implemented by us, the other results come from their papers.
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ChengYiBin
commented on July 1, 2024
thank u for reply : )
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Related Issues (20)
- Clarification re. Fixed Weight (FW) implementation HOT 3
- eval of each person for ntu dataset HOT 3
- About UWA3DII dataset HOT 2
- UWA dataset HOT 2
- pytorch implementation HOT 8
- Pretrained model on NTU-CS HOT 1
- Rotation Matrix R HOT 2
- error with get_feature() when run the train.py HOT 2
- some question on KNN HOT 2
- what's the shape of data at every HOT 2
- Train on 2D skeleton dataset HOT 4
- UWA3D handling HOT 1
- Action to predict HOT 5
- UCLA Data HOT 2
- About encoder states trajectories visualization HOT 2
- About problem in running ucla_demo HOT 1
- Your method cannot be called unsupervised!
- Is something wrong in pytorch implementation HOT 4
- Dimension of the hidden layer HOT 4
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