Global Average Pooling (GAP) is used by default on the channel-wise attention mechanism to extract channel descriptors. However, the simple global aggregation method of GAP is easy to make the channel descriptors have homogeneity, which weakens the detail distinction between feature maps, thus affecting the performance of the attention mechanism. In this work, we propose a novel method for channel-wise attention network, called Stochastic Region Pooling (SRP), which makes the channel descriptors more representative and diversity by encouraging the feature map to have more or wider important feature responses. Also, SRP is the general method for the attention mechanisms without any additional parameters or computation. It can be widely applied to attention networks without modifying the network structure. Experimental results on image recognition datasets including CIAFR-10 100, ImageNet and three Fine-grained datasets (CUB-200-2011, Stanford Cars and Stanford Dogs) show that SRP brings the significant improvements of the performance over efficient CNNs and achieves the state-of-the-art results.
. @math Channel-wise attention mechanisms have developed rapidly in recent years @cite_42 @cite_4 @cite_6 @cite_14 @cite_27 @cite_51 and the channel descriptors are crucial to them. In order to extract more representative descriptors, CBAM @cite_51 combines the output of the global max pooling and the global average pooling as the pooling method, and GEnet @cite_27 uses the depth-wise convolution with large kernel to replace GAP. However, the global max pooling in CBAM is prone to network overfitting @cite_21 , and CBAM also cannot enhance the channel descriptor with more spatial details. Besides, GENet will brings a lot of additional parameters or computation. Different from them, SRP is a training method that can encourage the descriptors to have more information about the feature map details. The reason why SRP uses GAP instead of the above methods to extract the descriptor is not only because GAP is simple and does not bring any additional parameters, but also that GAP is widely used for attention networks. This enables SRP to be conveniently used on these networks without modifying the network structure.
@cite_14 @cite_4 @cite_42 @cite_21 @cite_6 @cite_27 @cite_51
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Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. 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We validate our CBAM through extensive experiments on ImageNet-1K, MS COCO detection, and VOC 2007 detection datasets. Our experiments show consistent improvements in classification and detection performances with various models, demonstrating the wide applicability of CBAM. The code and models will be publicly available.