Scale channel attention network for image segmentation

The object scale variation results in a negative effect on image segmentation performance. Spatial pyramid pooling module or the attention mechanism are two widely used components in deep neural networks to handle this problem. Applying the single component commonly achieves limited benefit. To push the limit, in this paper, we propose a scale channel attention network (SCA-Net), which enhances the fusion feature of multi-scale by using channel attention components. After the multiple-scale pooling step, the multi-scale spatial information distributes in different feature channels. Meanwhile, the channel attention block is employed to guide SCA-Net focus on the object-relevant scale channels. We further explore the channel attention block and find a simple yet effective structure to combine global average pooling and global maximum pooling, resulting in a robust global information encoder. The SCA-Net does not contain any time-consuming post-processing, which is an extra step after the neural network for the segmentation result optimization. The assessment results on PASCAL VOC 2012 and Cityscapes benchmarks achieve the test set performance of 75.5% and 77.0%.