Comments (1)
@chrismcruiz
Hi, the released model is based on the proposed VAC, which is the non-iterative approach. We have not conducted cross dataset experiments yet. From my understanding, the feature extractor can be applied on another dataset, you need to change the dataloader and adopt the "features" mode, which will extract frame-wise features.
However, the posture distribution from different datasets may be different, maybe it will not achieve optimal performance, it is better to finetune or train on another dataset.
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Related Issues (20)
- Torch not compiled with CUDA enabled HOT 2
- Error when i run the training part main.py HOT 1
- unable to run successfully when I run main.py HOT 1
- unable to successfully install CTC HOT 5
- Detailed code running steps HOT 19
- Visualizing the predicted alignments HOT 1
- unable to run these repository on google colab HOT 1
- Data augmention error HOT 4
- about the feature extractor architecture HOT 6
- Successfully inference, but unable to train HOT 3
- Visualize Epoch vs Loss HOT 1
- Hardware and Software Specifications for this research. HOT 3
- Gloss Segment Boundary Assignment Algorithm (GSBA) HOT 1
- Does your PHOENIX-2014-T dataset have /1/ folder? HOT 1
- model or model.parameters()? HOT 2
- Does your model produce different results each time it is run? HOT 4
- Training the baseline (without VAC, SMKD) HOT 2
- How can I use a trained model to test a video?
- unable to reconize any word but the loss is decreasing??? HOT 1
- Does preprocess.sh have an effect if using datasets other than phoneix?
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