MCCL

[TCSVT 2025] Code for our paper MCCL: Facial Depression Estimation via Multi-Cue Contrastive Learning

Prerequisites

• Python3
• numpy == 1.24.4
• PyTorch == 1.13.1 (with CUDA and CuDNN (cu116))
• torchvision==0.14.1 (cu116)
• scikit-learn == 1.3.2
• xgboost == 2.0.3

Please create and activate the following conda environment. To reproduce our results, please kindly create and use this environment.

# It may take several minutes for conda to solve the environment
conda update conda
conda env create -f environment.yml
conda activate mccl

Train MCCL model

The program can be run with the default parameters using the following:

#Train for DAIC-WOZ
cd mccl
python main.py --dataset='DAIC' --output_name='daic'

Test MCCL model

The code was tested on an RTX 3090.

Please follow the below steps to test MCCL model on DAIC-WOZ dataset.

1. We have put checkpoint files in mccl/checkpoint/DAIC folder.
2. run python main.py --dataset='DAIC' --inference=1 to test the model on DAIC-WOZ dataset.

Citation

Please cite our work if you find it useful.

@ARTICLE{10852375,
  author={Wang, Xinke and Xu, Jingyuan and Sun, Xiao and Li, Mingzheng and Hu, Bin and Qian, Wei and Guo, Dan and Wang, Meng},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={Facial Depression Estimation via Multi-Cue Contrastive Learning}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  keywords={Depression;Estimation;Contrastive learning;Visualization;Correlation;Facial features;Circuits and systems;Three-dimensional displays;Feature extraction;Interviews;Facial depression estimation;multi-cue;contrastive learning},
  doi={10.1109/TCSVT.2025.3533543}}

Acknowledgement

The data processing of DAIC-WOZ dataset is based on the code DepressionEstimation(DAIC).

Data links

The Dataset can be applied from: here

The files should be unzipped, and the features should be extracted from the unzipped folder according to the code in 'mccl/data_processing'.