基于事件驱动的MLP架构，视频处理时只计算变化区域，省算力
evMLP: An Efficient Event-Driven MLP Architecture for Vision
- 用事件触发机制，仅处理图像中变化的局部区域
- 视频任务中计算量降低，精度与主流模型相当
- 适合实时视频分析、低功耗设备部署
深度神经网络在计算机视觉任务中表现卓越。早期以卷积神经网络（CNN）为主流，近年视觉变换器（ViTs）日益流行。与此同时，多层感知机（MLPs）在视觉模型架构中的探索也带来新视角。本文提出 evMLP 及其简单的事件驱动局部更新机制。evMLP 通过 MLP 独立处理图像或特征图上的各个块。我们定义连续帧间的差异为「事件」，在事件驱动的局部更新机制下，evMLP 仅对发生事件的区域进行处理。对于序列图像数据（如视频处理），该方法通过避免冗余计算提升计算效率。ImageNet 图像分类实验表明，evMLP 达到与先进模型相当的准确率。更显著的是，在多个视频数据集上的实验结果表明，evMLP 在保持输出一致性的同时，通过事件驱动机制显著降低计算成本。代码与预训练模型已公开于 https://github.com/i-evi/evMLP。
原文摘要 · Abstract (English)
Deep neural networks have achieved remarkable results in computer vision tasks. In the early days, Convolutional Neural Networks (CNNs) were the mainstream architecture. In recent years, Vision Transformers (ViTs) have become increasingly popular. In addition, exploring applications of multi-layer perceptrons (MLPs) has provided new perspectives for research into vision model architectures. In this paper, we present evMLP accompanied by a simple event-driven local update mechanism. The proposed evMLP can independently process patches on images or feature maps via MLPs. We define changes between consecutive frames as ``events''. Under the event-driven local update mechanism, evMLP selectively processes patches where events occur. For sequential image data (e.g., video processing), this approach improves computational performance by avoiding redundant computations. Through ImageNet image classification experiments, evMLP attains accuracy competitive with state-of-the-art models. More significantly, experimental results on multiple video datasets demonstrate that evMLP reduces computational cost via its event-driven local update mechanism while maintaining output consistency with its non-event-driven baseline. The code and pre-trained models are available at https://github.com/i-evi/evMLP.
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