eddiehe99 / pytorch-expression-spotting

The PyTorch repository for paper SL-Swin: A Transformer-Based Deep Learning Approach for Macro- and Micro-Expression Spotting on Small-Size Expression Datasets.

SL-Swin: A Transformer-Based Deep Learning Approach for Macro- and Micro-Expression Spotting on Small-Size Expression Datasets

Performance

MEGC 2022 Spotting Task

Method	3D-CNN	Swin-T	SL-Swin-T
p	---	0.60	0.60
CAS_Test Precision	0.4000	0.1521	0.1944
CAS_Test Recall	0.1111	0.1944	0.1944
CAS_Test F1-score	0.1739	0.1707	0.1944
SAMM_Test Precision	0.0845	0.0638	0.0689
SAMM_Test Recall	0.1935	0.0967	0.1290
SAMM_Test F1-score	0.1176	0.0769	0.0898
Overall Precision	0.1235	0.1075	0.1170
Overall Recall	0.1493	0.1492	0.1641
Overall F1-score	0.1351	0.1250	0.1366

MEGC 2021 Spotting Task

Method	3D-CNN	SL-Swin-T
p	---	0.60
CAS(ME)^2 MaE	0.2145	0.2236
CAS(ME)^2 ME	0.0714	0.0879
CAS(ME)^2 Overall	0.1675	0.1824
SAMM_longvideos MaE	0.1595	0.1675
SAMM_longvideos ME	0.04665	0.1044
SAMM_longvideos Overall	0.1084	0.1357

Research Articles

Peer-Reviewed Article (Recommended)

He, E.; Chen, Q.; Zhong, Q. SL-Swin: A Transformer-Based Deep Learning Approach for Macro- and Micro-Expression Spotting on Small-Size Expression Datasets. Electronics 2023, 12, 2656. https://doi.org/10.3390/electronics12122656

Preprint Article

He, E.; Chen, Q.; Zhong, Q. SL-Swin: A Transformer-Based Deep Learning Approach for Macro- and Micro-Expression Spotting on Small-Size Expression Datasets. Preprints.org 2023, 2023060079. https://doi.org/10.20944/preprints202306.0079.v2

Differences

1. In the peer-reviewed article, the present continuous tense is revised to the past continuous tense.
2. In the peer-reviewed article, the structure of the section Performance and section Discussion is revised.
3. In the peer-reviewed article, the word "pre-process" is revised to "preprocess".
4. In the peer-reviewed article, the phrase "in the task" is revised to "on the task".

WeChat Article in Chinese

华南师范大学：一种在小数据量的表情数据集上基于Transformer的表情检测方法 | MDPI Electronics

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Baseline Articles

ACM Article

Chuin Hong Yap, Moi Hoon Yap, Adrian Davison, Connah Kendrick, Jingting Li, Su-Jing Wang, and Ryan Cunningham. 2022. 3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame. In Proceedings of the 30th ACM International Conference on Multimedia (MM '22). Association for Computing Machinery, New York, NY, USA, 7016–7020. https://doi.org/10.1145/3503161.3551570

arXiv Article

Yap, C.H.; Yap, M.H.; Davison, A.K.; Kendrick, C.; Li, J.; Wang, S.; Cunningham, R. 3D-CNN for Facial Micro- and Macro-Expression Spotting on Long Video Sequences Using Temporal Oriented Reference Frame. arXiv e-prints 2021, arXiv:2105.06340, doi:https://doi.org/10.48550/arXiv.2105.06340.

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Related Repositories

TensorFlow Code

https://github.com/eddiehe99/tensorflow-expression-spotting

dlib-whl

You could find wheel package files of dlib for python of different versions on Windows_x64 at https://github.com/eddiehe99/dlib-whl.

Mean Average Precision for Evaluation (Official)

https://github.com/bes-dev/mean_average_precision

Survey about Articles and Codes (Official)

https://github.com/pakchoi-php/halo

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Acknowledgement

SOFTNet (Official)

Deep appreciation to Liong et al. for sharing their code at https://github.com/genbing99/SoftNet-SpotME.

Vision Transformer for Small-Size Datasets (Official)

https://github.com/aanna0701/SPT_LSA_ViT

Tutorials (in Chinese)

Deep appreciation to WeZhe for his tutorials about deep learning for image processing and his code at https://github.com/WZMIAOMIAO/deep-learning-for-image-processing.

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PS

The code is formatted by black.

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Contact

Please email me at [e-mail] if you have any inquiries or issues.