  Channel attention mechanisms in convolutional neural networks have been
proven to be effective in various computer vision tasks. However, the
performance improvement comes with additional model complexity and computation
cost. In this paper, we propose a light-weight and effective attention module,
called channel diversification block, to enhance the global context by
establishing the channel relationship at the global level. Unlike other channel
attention mechanisms, the proposed module focuses on the most discriminative
features by giving more attention to the spatially distinguishable channels
while taking account of the channel activation. Different from other attention
models that plugin the module in between several intermediate layers, the
proposed module is embedded at the end of the backbone networks, making it easy
to implement. Extensive experiments on CIFAR-10, SVHN, and Tiny-ImageNet
datasets demonstrate that the proposed module improves the performance of the
baseline networks by a margin of 3% on average.
