Comments (1)
Hi, we leverage the annonation to augment the training data. We show in our experiments that applying variable length augmentation enhances the robustness towards frame removals. You could skip this step by setting is_var_length
to False
at dataset.py.
from lipreading_using_temporal_convolutional_networks.
Related Issues (20)
- Can we do Sentence Prediction for the model? HOT 1
- About variable length augmentation HOT 1
- DC-TCN number of parameters and Hardest words list
- Must convert gray? HOT 1
- ShuffleNet's Parameter
- Do this code in github include the part of data Augmentation? HOT 1
- With the same data , why the result is so different on ms-tcn and dc-tcn ?
- Acc of resnet18_dctcn_video_boundary in my test is wrong HOT 1
- about preprocessing
- cant process HOT 1
- How to use pretrain model after download from Google drive HOT 2
- what is the form of <ANNONATION-DIRECTORY> because I want applied it own my dataset , and landmark method.
- IndexError: index 28 is out of bounds for axis 0 with size 4 when run crop_mouth_from_video.py
- RuntimeError: CUDA error: device-side assert triggered HOT 2
- Can you tell me how to get word boundary from real reasoning?
- Not able to evaluate visual-only performance using the pre-processed npz files HOT 2
- KeyError: 'optimizer_state_dict' arise with Pretrined model
- Your work is excellent! How can I calculate lip reading loss L between the face my model reders and my ground truth image?
- How to create .pkl file for my own video
- How are you dealing with varied length of input - like some are 29,28,27.
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from lipreading_using_temporal_convolutional_networks.