Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition
<|reference_start|>Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition: We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an event-frame multimodal teacher network. The core strength of this approach is its ability to utilize the ample, coarser temporal cues found in conventional frames for effective emotion recognition. Consequently, our method adeptly interprets both temporal and spatial information from the conventional frame domain, eliminating the need for specialized sensing devices, e.g., event-based camera. The effectiveness of our approach is thoroughly demonstrated using both existing and our compiled single-eye emotion recognition datasets, achieving unparalleled performance in accuracy and efficiency over existing state-of-the-art methods.<|reference_end|>
@article{wang2024apprenticeship-inspired,
    title={Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation
  Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition},
    author={Yang Wang, Haiyang Mei, Qirui Bao, Ziqi Wei, Mike Zheng Shou, Haizhou
  Li, Bo Dong, Xin Yang},
    journal={arXiv preprint arXiv:2407.09521},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09521},
    primaryClass={cs.CV cs.NE}
}