CBAM: Convolutional Block Attention Module[J]. 注意力机制大概分为以下，Spatial domain，Channel domain，Mixed domain以及Self-attention。接下来简单介绍这部分方法。1、通道注意力机制和空间注意力机制Convolutional Block Attention Module (CBAM) 表示卷积模块的注意力机制模块。是一种结合了空间（spatial）和通道（channel）的注意力机制模块。 We propose Convolutional Block Attention Module (CBAM), a simple yet effective attention module for feed-forward convolutional neural networks. Woo S, Park J, Lee J Y, et al. Structure. The codes are PyTorch re-implement version for paper: CBAM: Convolutional Block Attention Module. CBAM全称是Convolutional Block Attention Module, 是在ECCV2018上发表的注意力机制代表作之一。本人在打比赛的时候遇见过有人使用过该模块取得了第一名的好成绩，证明了其有效性。 Given an intermediate feature map, our module se-quentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature The module has two sequential sub-modules: channel and spatial. C-Mask-RCNN车位检测算法通过在Mask-RCNN算法的ResNet50特征提取网络中增加卷积块注意力模块（Convolutional Block Attention Module,CBAM）,使模型更加关注车位相关的语义信息。 Abstract. 2018. ECCV2018. The overview of CBAM.
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