  Attention mechanism is a hot spot in deep learning field. Using channel
attention model is an effective method for improving the performance of the
convolutional neural network. Squeeze-and-Excitation block takes advantage of
the channel dependence, selectively emphasizing the important channels and
compressing the relatively useless channel. In this paper, we proposed a
variant of SE block based on channel locality. Instead of using full connection
layers to explore the global channel dependence, we adopt convolutional layers
to learn the correlation between the nearby channels. We term this new
algorithm Channel Locality(C-Local) block. We evaluate SE block and C-Local
block by applying them to different CNNs architectures on cifar-10 dataset. We
observed that our C-Local block got higher accuracy than SE block did.
