Multi-Scale Spatially-Asymmetric Recalibration for Image Classification

Convolution is spatially-symmetric, i.e., the visual features are independent of its position in the image, which limits its ability to use spatial information. This paper addresses this issue by 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, besides introducing spatial asymmetry into convolution, extracts visual cues from regions at multiple scales to allow richer information to be incorporated. 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 network architectures, in which all convolutional layers are recalibrated, and demonstrate superior performance in both CIFAR and ILSVRC2012 classification tasks.