Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention modules have been proposed and implemented in various ways. Generally speaking, they are mainly based on convolution and pooling operations. In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way. The GPCA module intends to model the correlations among the channels, which are assumed to be captured by beta distributed variables. As the beta distribution cannot be integrated into the end-to-end training of convolutional neural networks (CNNs) with a mathematically tractable solution, we utilize an approximation of the beta distribution to solve this problem. To specify, we adapt a Sigmoid-Gaussian approximation, in which the Gaussian distributed variables are transferred into the interval [0,1]. The Gaussian process is then utilized to model the correlations among different channels. In this case, a mathematically tractable solution is derived. The GPCA module can be efficiently implemented and integrated into the end-to-end training of the CNNs. Experimental results demonstrate the promising performance of the proposed GPCA module. Codes are available at https://github.com/PRIS-CV/GPCA.

翻译：为了有效改进绩效,在许多视觉任务中通常采用频道关注机制,以有效改进性能,它可以加强信息渠道,压制无用渠道。最近,提出了不同的频道关注模块,并以各种方式实施。一般而言,这些模块主要基于聚合和集合操作。在本文中,我们建议高山进程嵌入频道关注模块,并进一步以概率方式解释频道关注计划。GPCA模块打算模拟频道之间的相互关系,这些关系被贝塔分布变量所捕捉。由于贝塔分布无法纳入具有数学可移植解决方案的革命神经网络端到端培训,因此我们使用贝塔分布的近似近似来解决这一问题。为了具体说明,我们调整了高萨进程嵌入的频道关注模块,将高萨分布的变量转换到间隔 [0,1]。然后,高萨进程用于模拟不同频道之间的关联。在这种情况下,将生成一个数学可移植的解决方案。GPCA/CRA模块在数学可移植的版本中可以有效地应用。GPCA/GRCA模块的模拟性能演示。GRCA。GRCA模块可以有效地在最终展示。