  Convolution is spatially-symmetric, i.e., the visual features are independent
of its position in the image, which limits its ability to utilize contextual
cues for visual recognition. This paper addresses this issue by introducing a
recalibration process, which refers to the surrounding region of each neuron,
computes an importance value and multiplies it to the original neural response.
Our approach is named multi-scale spatially-asymmetric recalibration (MS-SAR),
which extracts visual cues from surrounding regions at multiple scales, and
designs a weighting scheme which is asymmetric in the spatial domain. MS-SAR is
implemented in an efficient way, so that only small fractions of extra
parameters and computations are required. We apply MS-SAR to several popular
building blocks, including the residual block and the densely-connected block,
and demonstrate its superior performance in both CIFAR and ILSVRC2012
classification tasks.
