Unsupervised learning plays an important role in the neural networks. Focusing on the unsupervised mechanism of neural networks, a novel generalized goodness criterion for the unsupervised neural learning of visual pe...Unsupervised learning plays an important role in the neural networks. Focusing on the unsupervised mechanism of neural networks, a novel generalized goodness criterion for the unsupervised neural learning of visual perception based on the martingale measure is proposed in the paper. The differential geometrical structure is used as the framework of the whole inference and spatial statistical description with adaptive attribute is embedded in the corresponding nonlinear functional space. Consequently the integration of optimization process and computational simulation with the NeoDarwinian paradigm is obtained. And the generalization of the guidance for the evolutionary learning in the neural net framework, the convergence of the goodness and process of the evolution guaranteed by the mathematical features are discussed. This criterion has generic significance in the field of machine vision and visual pattern classification.展开更多
现有的注意力机制仅增强特征图的通道或空间维度,未能充分捕捉细微视觉元素和多尺度特征变化。为解决此问题,提出一种基于局部分块与全局多尺度特征融合的注意力机制(patch and global multiscale attention,PGMA)。将特征图分割成多个...现有的注意力机制仅增强特征图的通道或空间维度,未能充分捕捉细微视觉元素和多尺度特征变化。为解决此问题,提出一种基于局部分块与全局多尺度特征融合的注意力机制(patch and global multiscale attention,PGMA)。将特征图分割成多个小块,分别计算这些小块的注意力得分,增强对局部信息的感知能力。使用一组空洞卷积计算整个特征图的得分,获得全局多尺度信息的权衡。实验中,将PGMA集成到U-Net、DeepLab、SegNet等语义分割网络中,有效提升了它们的分割性能。这表明PGMA在增强CNN性能方面优于当前主流方法。展开更多
文摘Unsupervised learning plays an important role in the neural networks. Focusing on the unsupervised mechanism of neural networks, a novel generalized goodness criterion for the unsupervised neural learning of visual perception based on the martingale measure is proposed in the paper. The differential geometrical structure is used as the framework of the whole inference and spatial statistical description with adaptive attribute is embedded in the corresponding nonlinear functional space. Consequently the integration of optimization process and computational simulation with the NeoDarwinian paradigm is obtained. And the generalization of the guidance for the evolutionary learning in the neural net framework, the convergence of the goodness and process of the evolution guaranteed by the mathematical features are discussed. This criterion has generic significance in the field of machine vision and visual pattern classification.
文摘现有的注意力机制仅增强特征图的通道或空间维度,未能充分捕捉细微视觉元素和多尺度特征变化。为解决此问题,提出一种基于局部分块与全局多尺度特征融合的注意力机制(patch and global multiscale attention,PGMA)。将特征图分割成多个小块,分别计算这些小块的注意力得分,增强对局部信息的感知能力。使用一组空洞卷积计算整个特征图的得分,获得全局多尺度信息的权衡。实验中,将PGMA集成到U-Net、DeepLab、SegNet等语义分割网络中,有效提升了它们的分割性能。这表明PGMA在增强CNN性能方面优于当前主流方法。
