受学徒制启发的精妙设计：协同知识蒸馏赋能脉冲神经网络实现高效单眼情绪识别
Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition
AI总结：
本研究提出多模态协同知识蒸馏方案，让轻量单模态SNN从事件-帧多模态教师网络学习，无需专用设备即可利用常规帧的时空信息实现高效单眼情绪识别，在多数据集上准确率和效率均超现有最优水平。
摘要：
我们提出了一种专为高效单眼运动识别任务定制的新型多模态协同知识蒸馏方案。该方法让轻量级单模态学生脉冲神经网络（SNN，Spiking Neural Network）能够从事件-帧多模态教师网络中提取丰富知识。这一方法的核心优势在于能够利用常规帧中充足的、时间粒度更粗的线索实现有效情绪识别。因此，我们的方法能巧妙解读常规帧域中的时间与空间信息，无需依赖事件相机等专用传感设备。我们在现有数据集及自行构建的单眼情绪识别数据集上充分验证了方法的有效性，在准确率和效率上均超越现有最优方法，取得了前所未有的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
