Awesome
TGN
Tensorflow Implementation of the EMNLP-2018 paper Temporally Grounding Natural Sentence in Video by Jingyuan Chen et al.
Requirements
pip install -r requirements.txt
Data Preparation
- Download Glove word embedding data.
cd download/
sh download_glove.sh
- Download dataset features.
TACoS: BaiduDrive, GoogleDrive
Charades-STA: BaiduDrive, GoogleDrive
ActivityNet-Captions: BaiduDrive, GoogleDrive
Put the feature hdf5 file in the corresponding directory ./datasets/{DATASET}/features/
We decode TACoS/Charades videos using fps=16
and extract C3D (fc6) features for each non-overlap 16-frame snippet. Therefore, each feature corresponds to 1-second snippet. For ActivityNet, each feature corresponds to 2-second snippet. To extract C3D fc6 features, I mainly refer to this code.
- Download trained models.
Download and put the checkpoints in corresponding ./checkpoints/{DATASET}/
.
- Data Preprocessing (Optional)
cd datasets/tacos/
sh prepare_data.sh
Then copy the generated data in ./data/save/
.
Use correspondig scripts for preparing data for other datasets.
You may skip this procedure as the prepared data is already saved in ./datasets/{DATASET}/data/save/
.
Testing and Evaluation
sh scripts/test_tacos.sh
sh scripts/eval_tacos.sh
Use corresponding scripts for testing or evaluating for other datasets.
The predicted results are also provided in ./results/{DATASET}/
.
Training
sh scripts/train_tacos.sh
Use corresponding scripts for training for other datasets.