  In this work, we propose a generally applicable transformation unit for
visual recognition with deep convolutional neural networks. This transformation
explicitly models channel relationships with explainable control variables.
These variables determine the neuron behaviors of competition or cooperation,
and they are jointly optimized with the convolutional weight towards more
accurate recognition. In Squeeze-and-Excitation (SE) Networks, the channel
relationships are implicitly learned by fully connected layers, and the SE
block is integrated at the block-level. We instead introduce a channel
normalization layer to reduce the number of parameters and computational
complexity. This lightweight layer incorporates a simple l2 normalization,
enabling our transformation unit applicable to operator-level without much
increase of additional parameters. Extensive experiments demonstrate the
effectiveness of our unit with clear margins on many vision tasks, i.e., image
classification on ImageNet, object detection and instance segmentation on COCO,
video classification on Kinetics.
