  Following the advance of style transfer with Convolutional Neural Networks
(CNNs), the role of styles in CNNs has drawn growing attention from a broader
perspective. In this paper, we aim to fully leverage the potential of styles to
improve the performance of CNNs in general vision tasks. We propose a
Style-based Recalibration Module (SRM), a simple yet effective architectural
unit, which adaptively recalibrates intermediate feature maps by exploiting
their styles. SRM first extracts the style information from each channel of the
feature maps by style pooling, then estimates per-channel recalibration weight
via channel-independent style integration. By incorporating the relative
importance of individual styles into feature maps, SRM effectively enhances the
representational ability of a CNN. The proposed module is directly fed into
existing CNN architectures with negligible overhead. We conduct comprehensive
experiments on general image recognition as well as tasks related to styles,
which verify the benefit of SRM over recent approaches such as
Squeeze-and-Excitation (SE). To explain the inherent difference between SRM and
SE, we provide an in-depth comparison of their representational properties.
