提出双通道时序注意力机制，提升脉冲神经网络的时空信息捕捉能力。
DTA: Dual Temporal-channel-wise Attention for Spiking Neural Networks
- 融合相同与不同操作的双重时序注意力，捕捉时空关联与依赖。
- 在CIFAR10、CIFAR100、ImageNet-1k和CIFAR10-DVS上达到最优性能。
- 适用于需低功耗高效处理动态视觉数据的场景。
脉冲神经网络（SNNs）通过利用时空动态和事件驱动的脉冲，相较于人工神经网络（ANNs）展现出更优的能效表现。有效利用时序信息对SNN至关重要，促使研究者探索注意力机制以增强其能力。传统注意力操作在目标维度上要么采用相同操作，要么使用不同操作，我们发现这两种方式分别提供了对时序信息的不同视角。为结合两者优势，本文提出一种新型双时序-通道注意力（DTA）机制，同时整合相同与非相同注意力策略。据我们所知，这是首个同时关注时序-通道相关性与依赖性的方法。实验表明，DTA在静态数据集（CIFAR10、CIFAR100、ImageNet-1k）和动态数据集（CIFAR10-DVS）上均达到当前最佳性能，显著提升了脉冲表征能力并捕捉复杂时序-通道关系。代码已开源：https://github.com/MnJnKIM/DTA-SNN。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) present a more energy-efficient alternative to Artificial Neural Networks (ANNs) by harnessing spatio-temporal dynamics and event-driven spikes. Effective utilization of temporal information is crucial for SNNs, leading to the exploration of attention mechanisms to enhance this capability. Conventional attention operations either apply identical operation or employ non-identical operations across target dimensions. We identify that these approaches provide distinct perspectives on temporal information. To leverage the strengths of both operations, we propose a novel Dual Temporal-channel-wise Attention (DTA) mechanism that integrates both identical/non-identical attention strategies. To the best of our knowledge, this is the first attempt to concentrate on both the correlation and dependency of temporal-channel using both identical and non-identical attention operations. Experimental results demonstrate that the DTA mechanism achieves state-of-the-art performance on both static datasets (CIFAR10, CIFAR100, ImageNet-1k) and dynamic dataset (CIFAR10-DVS), elevating spike representation and capturing complex temporal-channel relationship. We open-source our code: https://github.com/MnJnKIM/DTA-SNN.
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