Scaled gated networks

Gating transformation demonstrates great potential in recent deep convolutional neural networks design, enriching the feature representation and alleviating noisy signals by modeling the inter-channel dependencies using learnable parameters. However, the utilization of scaling approaches to reduce the redundancy of the hand-crafted attention mechanism has rarely been investigated. This paper proposes a novel scaled gated convolution that enables attention-enhanced CNNs to overcome the paradox between performance and redundancy. Our scaled gated convolution is a simple and effective alternative compared with both vanilla convolution and attention-enhanced convolutions, which can be easily applied to modern CNNs in a plug-and-play manner. Exhaustive experiments demonstrate that stacking scaled gated convolutions in baselines can significantly improve the performance in a broad range of visual recognition tasks, including image recognition, object detection, instance segmentation, keypoint detection, and panoptic segmentation, while obtaining a better trade-off between performance and attentive redundancy.