Spiking Neural Networks (SNNs) have a low-power advantage but perform poorly in image segmentation tasks. The reason is that directly converting neural networks with complex architectural designs for segmentation tasks into spiking versions leads to performance degradation and non-convergence. To address this challenge, we first identify the modules in the architecture design that lead to the severe reduction in spike firing, make targeted improvements, and propose Spike2Former architecture. Second, we propose normalized integer spiking neurons to solve the training stability problem of SNNs with complex architectures. We set a new state-of-the-art for SNNs in various semantic segmentation datasets, with a significant improvement of +12.7% mIoU and 5.0 efficiency on ADE20K, +14.3% mIoU and 5.2 efficiency on VOC2012, and +9.1% mIoU and 6.6 efficiency on CityScapes.

翻译：脉冲神经网络（SNNs）具有低功耗优势，但在图像分割任务中表现不佳。其原因是，直接将用于分割任务的具有复杂架构设计的神经网络转换为脉冲版本会导致性能下降和不收敛。为应对这一挑战，我们首先识别了导致脉冲发放严重减少的架构设计模块，进行了针对性改进，并提出了Spike2Former架构。其次，我们提出了归一化整数脉冲神经元，以解决具有复杂架构的SNNs的训练稳定性问题。我们在多个语义分割数据集上为SNNs设立了新的最先进性能，在ADE20K上实现了+12.7% mIoU和5.0效率的显著提升，在VOC2012上实现了+14.3% mIoU和5.2效率的提升，在CityScapes上实现了+9.1% mIoU和6.6效率的提升。