Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition
<|reference_start|>Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge Distillation for Micro-Expression Recognition: Micro-expressions are subtle facial movements that occur spontaneously when people try to conceal real emotions. Micro-expression recognition is crucial in many fields, including criminal analysis and psychotherapy. However, micro-expression recognition is challenging since micro-expressions have low intensity and public datasets are small in size. To this end, a three-stream temporal-shift attention network based on self-knowledge distillation called SKD-TSTSAN is proposed in this paper. Firstly, to address the low intensity of muscle movements, we utilize learning-based motion magnification modules to enhance the intensity of muscle movements. Secondly, we employ efficient channel attention modules in the local-spatial stream to make the network focus on facial regions that are highly relevant to micro-expressions. In addition, temporal shift modules are used in the dynamic-temporal stream, which enables temporal modeling with no additional parameters by mixing motion information from two different temporal domains. Furthermore, we introduce self-knowledge distillation into the micro-expression recognition task by introducing auxiliary classifiers and using the deepest section of the network for supervision, encouraging all blocks to fully explore the features of the training set. Finally, extensive experiments are conducted on four public datasets: CASME II, SAMM, MMEW, and CAS(ME)3. The experimental results demonstrate that our SKD-TSTSAN outperforms other existing methods and achieves new state-of-the-art performance. Our code will be available at https://github.com/GuanghaoZhu663/SKD-TSTSAN.<|reference_end|>
@article{zhu2024three-stream,
    title={Three-Stream Temporal-Shift Attention Network Based on Self-Knowledge
  Distillation for Micro-Expression Recognition},
    author={Guanghao Zhu and Lin Liu and Yuhao Hu and Haixin Sun and Fang Liu and
  Xiaohui Du and Ruqian Hao and Juanxiu Liu and Yong Liu and Hao Deng and Jing
  Zhang},
    journal={arXiv preprint arXiv:2406.17538},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17538},
    primaryClass={cs.CV}
}