ME-TST

Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition [ICASSP 2025]

Framework:

Visualization results

🔧 Setup

STEP1: bash setup.sh

STEP2: conda activate ME-TST

STEP3: pip install -r ./requirements.txt

💻 Example of Using Pre-trained Models

If you want to run the pre-trained model on SAMMLV, use python main.py --dataset_name SAMMLV --train False --flow_process False

💻 Examples of Neural Network Training

STEP 1: Download the $CAS(ME)^3$ raw data by asking the paper authors

STEP 2: Modify main.py; load_excel.py; load_images.py

STEP 3: Run python main.py --dataset_name CASME_3 --train True --flow_process True

🎓 Acknowledgement

We referred to MEAN_Spot-then-recognize, and would like to express our sincere thanks to the authors.

📜 Citation

If you find this repository helpful, please consider citing:

@article{zou2024synergistic,
  title={Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition},
  author={Zou, Bochao and Guo, Zizheng and Qin, Wenfeng and Li, Xin and Wang, Kangsheng and Ma, Huimin},
  journal={arXiv preprint arXiv:2409.09707},
  year={2024}
}