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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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语义细分的多尺度全尖峰模式的语义细分.

Qiaoyi Su1, Weihua He2, Xiaobao Wei3

  • 1School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

Neural networks : the official journal of the International Neural Network Society
|April 30, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了MFS-Seg,这是一个用于语义细分的新型尖端神经网络 (SNN). 该模型实现了与传统网络相当的性能,同时大幅降低了参数和能源消耗,突出了SNNs.

关键词:
大脑启发的计算深度神经网络是一个神经网络.能源效率 能源效率是指能源的使用效率.神经形态计算是一种神经形态计算.语义细分 语义细分是指语义细分.尖的神经网络的神经网络.

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科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 神经科学是一个神经科学.

背景情况:

  • 尖端神经网络 (SNN) 提供低功耗,以大脑为灵感的计算,但在语义细分任务中面临挑战.
  • 现有的语义细分SNN模型表现出不良性能和高内存开销,限制了它们的实际应用.
  • 用代用梯度对SNN进行深度直接培训是一个新兴的领域,有可能提高效率.

研究的目的:

  • 为像素级语义细分开发一个高效的基于SNN的模型.
  • 为了解决当前SNN细分模型的性能限制和高内存/能量开销.
  • 为了证明深度SNNs在需要精确空间理解的计算机视觉任务中的有效性.

主要方法:

  • 提出了多尺度和全尖端细分网络 (MFS-Seg),这是一个使用替代梯度训练的深度SNN.
  • 引入了一种高效的全尖剩余块 (EFS-Res),使用深度可分离的卷积来处理尖尖噪声并改善特征表示.
  • 理论上分析了EFS-Res使用区块动态同度理论来防止性能降低.

主要成果:

  • 在基准数据集上,MFS-Seg实现了与主流UNet架构 (Camvid,DDD17,DSEC-Semantic) 相同的语义细分性能.
  • 该模型显示了显著减少内存开销 (减少了多达31倍的参数) 和功耗 (减少了13倍以上).
  • 可视化证实了MFS-Seg有效提取对象边缘特征的能力.

结论:

  • 在将深度SNN应用于语义细分任务方面,MFS-Seg代表了重大进展.
  • 拟议的EFS-Res块有效地减轻了SNN特定的挑战,提高了性能和效率.
  • 这些发现强调了SNNs作为复杂计算机视觉应用的可行,低功耗替代品的潜力.