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English(EN) Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer

新方法增强 Spiking Transformer 在图像任务上的性能 · 跟踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员引入了尖峰局部交互 (SLI) 和自适应互补融合 (ACF) 来增强 Spiking Transformer。这些方法通过引入一个独立于注意力的通道，用于相邻尖峰令牌之间的直接信息交换，并自适应地平衡 SSA 和 SLI 的贡献，从而解决了标准尖峰自注意力 (SSA) 的局限性。在包括 ImageNet-1K 和 ADE20K 在内的各种数据集上的实验表明，在图像分类、基于事件的识别和语义分割方面取得了持续的改进。 AI

影响 这些进展可能为各种计算机视觉任务带来更高效、更有效的尖峰神经网络。

排序理由 该集群包含一篇详细介绍 Spiking Transformer 新方法的论文。

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新方法增强 Spiking Transformer 在图像任务上的性能 · 跟踪 2 个来源

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该集群包含一篇详细介绍 Spiking Transformer 新方法的论文。
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报道来源 [2]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Tielin Zhang ·

    Spiking Local Interaction and Adaptive Complementary Fusion for Spiking Transformer

    Spiking Transformers model token interactions primarily through spiking self-attention (SSA). However, binary query and key representations map continuous similarities to sparse and discrete relation responses, which may suppress weak relations and limit the propagation of local …

  2. arXiv cs.CV TIER_1 English(EN) · Dongcheng Zhao, Sicheng Shen, Zhenyu Yang, Zhiyuan Li, Jinyan Yu, Yongjian Wang, Tiechui Yao, Wenli Zhang, Tielin Zhang ·

    Spiking Transformer 的尖峰局部交互和自适应互补融合

    arXiv:2608.19238v1 Announce Type: cross Abstract: Spiking Transformers model token interactions primarily through spiking self-attention (SSA). However, binary query and key representations map continuous similarities to sparse and discrete relation responses, which may suppress …

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