This repo contains code and data used in the BMVC paper 'Learning Temporal Sentence Grounding From Narrated EgoVideos'.
The Omnivore visual features and BERT text features for the Ego4D and EPIC-Kitchens dataset splits will be released soon.
The dependencies can be found in Climer.yml
This is a conda environment and can be installed with the following command:
conda env create -f climer.yml
The 'data' folder contains csv files with the annotations for the train, validation and test splits for Ego4D and EPIC-Kitchens. It also contains metadata files which contain extra information used during training/testing.
The Omnivore visual features and the BERT features should be placed in the 'features' directory. Alternatively change the path to your chosen location for the features in the config file(s).
Pretrained models will be released soon.
To train the model, run the following command, replacing $DATASET with 'ego4d' for the Ego4D dataset and 'epic' for the EPIC-Kitchens dataset
bash train.sh $DATASET
Model checkpoints are saved by default in the 'checkpoints' directory.
To evaluate the model, run the following command, again replacing $DATASET with the corresponding dataset (ego4d or epic)
bash test.sh $DATASET