Comments (1)
Yes, even though MS-G3D performs slightly better for recognition on the NTU-60 dataset, I think its the combination of Shift-GCN embeddings with the Sentence-BERT embeddings, which works better for ZSL using our approach.
The training time is similar for both shift gcn and msg3d embeddings, although msg3d embeddings are slightly higher dimensional.(384 > 256)
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Related Issues (14)
- Rename num_epoch_per_cycle to num_cycle_per_epoch HOT 1
- Extra spaces in examples HOT 1
- How to replicate ablation test with MS-G3D? HOT 3
- RuntimeError: DataLoader worker (pid(s) 27447) exited unexpectedly HOT 3
- [Questions] Only calling Dataloader once per epoch? Training classifier from scratch at each cycle? HOT 1
- What is gtest_out.npy? HOT 7
- In synse_resources rs10.npy == rs100.npy HOT 2
- What is synse_10_r_unseen_zs.npy and synse_10_r_seen_zs.npy HOT 5
- Confused about some folders HOT 7
- some questions about visual feature extractor HOT 5
- Is there any Inference to test on an Image or a video? HOT 2
- NTU split
- What is val*.npy, where does it come from, and how do you determined the sample size of it?
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