Comments (4)
@hulianyuyy
There are two widely used ways to align the labels with features, the greedy alignment (selecting the most likely gloss at each step) and the dominant alignment (DNF, TMM'19). We adopt the greedy alignment for simplicity and show the network predictions.
The alignment between features and frames is simply based on its temporal receptive field, for example, the temporal receptive field of Subgloss-wise conv1d (C5-P2) is 6, so the logit z_t is corresponding to frames f_[t2, t2+6] (no padding setting).
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A question that confused me is how to find the ground truth label of frames (as marked in Fig.5 in your paper). As far as we know, the Phoenix Dataset only provide labels in video domain but not precisely assigned to frames.
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@hulianyuyy
We mannually labelled several sequences for visualization. (This website may be helpful for Phoenix).
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Many thanks for you kind reply.
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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? HOT 1
- unable to reconize any word but the loss is decreasing??? HOT 1
- Does preprocess.sh have an effect if using datasets other than phoneix? HOT 1
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