Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition (NeurIPS 2022)
CVIP Lab, Inha University
To install all dependencies, do this.
pip install -r requirements.txt
Download four public benchmarks for training and evaluation (please download after agreement accepted).
(For more details visit website)
Follow preprocessing rules for each dataset by referring pytorch official custom dataset tutorial.
Just run the below script!
chmod 755 run.sh
./run.sh <method> <gpu_no> <port_no>
<method>
: elim
or elim_category
<gpu_no>
: GPU number such as 0 (or 0, 1 etc.)<port_no>
: port number to clarify workers (e.g., 12345)age_script
folder to your train or val. script and turn on elim_category
option.print_check
point in training phase.demo
folder, and then feel free to use.
If our work is useful for your work, then please consider citing below bibtex:
@misc{kim2022elim,
author = {Kim, Daeha and Song, Byung Cheol},
title = {Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition},
Year = {2022},
Eprint = {arXiv:2209.12172}
}
If you have any questions, feel free to contact me at kdhht5022@gmail.com
.