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arXiv · 1804.00787

Multi-Scale Spatially-Asymmetric Recalibration for Image Classification

Yan Wang·Lingxi Xie·Siyuan Qiao·Ya Zhang·Wenjun Zhang·Alan L. Yuille
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Abstract

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.

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Yan Wang, Lingxi Xie, Siyuan Qiao, Ya Zhang, Wenjun Zhang, Alan L. Yuille. 2018-04-03. Multi-Scale Spatially-Asymmetric Recalibration for Image Classification. https://arxiv.org/abs/1804.00787

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