SRM : A Style-based Recalibration Module for Convolutional Neural Networks
This work addresses performance enhancement for CNNs in vision tasks, but it appears incremental as it builds on existing style transfer and recalibration methods like SE.
The paper tackles the problem of improving CNN performance in general vision tasks by leveraging style information, proposing a Style-based Recalibration Module (SRM) that enhances representational ability with negligible overhead and shows benefits over approaches like Squeeze-and-Excitation (SE) in experiments.
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.