Update README.md

README.md

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-license: mit
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+# [Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data](https://arxiv.org/pdf/2409.06154)
+<img width="1024" height="506" alt="image" src="https://github.com/user-attachments/assets/db750330-84e2-4128-96c3-77c4a8fdc76c" />
+
+## 📰 News
+
+**[2025.9.17]** Our previous work [S2D](https://github.com/MSA-LMC/S2D/tree/main) has been recognized as a Highly Cited Paper by Clarivate.
+
+**[2025.9.17]** The code and pre-trained models are available.
+
+**[2025.9.15]** The paper is accepted by the IEEE Transactions on Affective Computing.
+
+~~[2024.9.5] Code and pre-trained models will be released here.~~
+
+## 🚀 Main Results
+
+<img width="1024" alt="image" src="https://github.com/user-attachments/assets/31b131e1-6530-4486-9bb4-a006fe464d32" />
+
+<img width="1024" height="464" alt="image" src="https://github.com/user-attachments/assets/41904e7a-31cb-4025-badc-4fdc979b1763" />
+
+<img width="1024" height="377" alt="image" src="https://github.com/user-attachments/assets/237962f6-4aa8-4855-b7d0-306df5d0ee73" />
+
+
+## Pre-Training and Fine-Tune
+1、 Download the pre-trained weights from [Huggingface](https://huggingface.co/cyinen/S4D), and move it to the [finetune/checkpoints/pretrain/voxceleb2+AffectNet] directory.
+
+2、 Run the following command to pre-train or fine-tune the model on the target dataset.
+
+```bash
+# create the envs
+conda create -n s4d python=3.9
+conda activate s4d
+pip install -r requirements.txt
+
+# pre-train
+cd pretrain/omnivision &&  OMP_NUM_THREADS=1 HYDRA_FULL_ERROR=1 python train_app_submitit.py +experiments=videomae/videomae_base_vox2_affectnet
+
+# fine-tune
+cd finetune && bash run.sh
+```
+
+## ✏️ Citation
+
+If you find this work helpful, please consider citing:
+```bibtex
+@ARTICLE{10663980,
+  author={Chen, Yin and Li, Jia and Shan, Shiguang and Wang, Meng and Hong, Richang},
+  journal={IEEE Transactions on Affective Computing}, 
+  title={From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={1-15},
+  keywords={Adaptation models;Videos;Computational modeling;Feature extraction;Transformers;Task analysis;Face recognition;Dynamic facial expression recognition;emotion ambiguity;model adaptation;transfer learning},
+  doi={10.1109/TAFFC.2024.3453443}}
+
+@ARTICLE{11207542,
+  author={Chen, Yin and Li, Jia and Zhang, Yu and Hu, Zhenzhen and Shan, Shiguang and Wang, Meng and Hong, Richang},
+  journal={IEEE Transactions on Affective Computing}, 
+  title={Static for Dynamic: Towards a Deeper Understanding of Dynamic Facial Expressions Using Static Expression Data},