GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention
- Publication Type:
- Journal Article
- Citation:
- 2020
- Issue Date:
- 2020-03-10
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Filename | Description | Size | |||
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2003.04575v2.1.pdf | Accepted version | 5.39 MB |
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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.
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