标题： GPCA：高斯过程嵌入通道注意力的概率框架 显示英文标题

标题： GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention

摘要： 通道注意力机制已被广泛应用于许多视觉任务中，以有效提升性能。它能够增强有信息量的通道并抑制无用的通道。最近，不同的通道注意力模块已被提出并以各种方式实现。一般来说，它们主要基于卷积和池化操作。在本文中，我们提出了嵌入高斯过程的通道注意力（GPCA）模块，并进一步以概率方式解释通道注意力方案。GPCA模块旨在建模通道之间的相关性，这些相关性被假设由贝塔分布变量捕获。由于贝塔分布无法以数学上可处理的方式集成到卷积神经网络（CNNs）的端到端训练中，我们利用贝塔分布的近似来解决这个问题。具体来说，我们采用了一种Sigmoid-高斯近似，其中高斯分布变量被转换到区间[0,1]。然后利用高斯过程来建模不同通道之间的相关性。在这种情况下，得到了一个数学上可处理的解决方案。GPCA模块可以高效实现并集成到CNNs的端到端训练中。实验结果证明了所提出的GPCA模块的出色性能。代码可在https://github.com/PRIS-CV/GPCA获取。 显示英文摘要

摘要： 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.