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.

我们介绍了一种新颖的多模态协同知识蒸馏方案，专为高效的单眼动作识别任务而设计。这种方法允许轻量级的单模态脉冲神经网络（SNN）从事件帧多模教师网络中提取丰富的知识。这种方法的核心优势在于能够利用传统帧中丰富而更粗糙的时间线索来进行有效的情感识别。因此，我们的方法熟练地解释了传统帧领域中的时间和空间信息，避免了对专用传感设备（例如事件相机）的需求。通过对现有和我们编制的单眼情感识别数据集进行全面展示，我们的方法在准确性和效率方面实现了卓越的性能，超越了现有的最先进方法。

启发式学徒技术：协同知识蒸馏为高效单眼情绪识别增添能力

Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation  Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition

Bio-inspired Address Event Representation (AER) sensors have attracted
significant popularity owing to their low power consumption, high sparsity, and
high temporal resolution. Spiking Neural Network (SNN) has become the inherent
choice for AER data processing. However, the integration of the AER-SNN
paradigm has not adequately explored asynchronous processing, neuromorphic
compatibility, and sparse spiking, which are the key requirements of
resource-constrained applications. To address this gap, we introduce a
brain-inspired AER-SNN object recognition solution, which includes a data
encoder integrated with a First-To-Spike recognition network. Being fascinated
by the functionality of neurons in the visual cortex, we designed the solution
to be asynchronous and compatible with neuromorphic hardware. Furthermore, we
have adapted the principle of denoising and First-To-Spike coding to achieve
optimal spike signaling, significantly reducing computation costs. Experimental
evaluation has demonstrated that the proposed method incurs significantly less
computation cost to achieve state-of-the-art competitive accuracy. Overall, the
proposed solution offers an asynchronous and cost-effective AER recognition
system that harnesses the full potential of AER sensors.

利用神经元在视皮层的功能特性，我们介绍了一种异步且与神经形态硬件兼容的 AER-SNN 对象识别解决方案，该解决方案采用数据编码器与首次发放脉冲识别网络集成，通过降噪和首次发放编码的原则实现了最佳脉冲信号传递，显著降低计算成本，并通过实验证明，所提出的方法在获得最先进的准确性的同时大大降低了计算成本。该解决方案为异步和具有成本效益的 AER 识别系统，充分发挥了 AER 传感器的潜力。