  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.
