This is the official source code of our ITV paper: (Un)likelihood Training for Interpretable Embedding.
We used Anaconda to setup a workspace with PyTorch 1.8. Run the following script to install the required packages.
conda create -n ITV python==3.8 -y
conda activate ITV
git clone https://github.com/nikkiwoo-gh/ITV.git
cd ITV
pip install -r requirements.txt
./do_install_StanfordCoreNLIP.sh
See the data page.
ITV trained on tgif-msrvtt10k-VATEX
./do_get_vocab_and_concept.sh $collection
e.g.,
./do_get_vocab_and_concept.sh tgif-msrvtt10k-VATEX
See the data page.
./do_train_ITV.sh
./do_predition_iacc.3_ITV.sh
./do_predition_v3c1_ITV.sh
./do_predition_v3c2_ITV.sh
Remember to set the score_file correctly to your own path.
cd tv-avs-eval/
do_eval_iacc.3.sh
do_eval_v3c1.sh
do_eval_v3c2.sh
@inproceedings{ACMTOIS2023_ITV,
author = {Wu, Jiaxin and Ngo, Chong-Wah and Chan, Wing-Kwong and Hou, Zhijian},
title = {(Un)likelihood Training for Interpretable Embedding},
year = {2023},
volume = {42},
number = {3},
journal = {ACM Transactions on Information Systems},
pages = {1-26},
}
If you have any questions, please feel free to contact me
- Jiaxin Wu ([email protected])