Lightweight Self-Attention Network for Semantic Segmentation

The deep neural network model based on self-attention (SA) for obtaining rich contextual information has been widely adopted in semantic segmentation. However, the computational complexity of the standard self-attentive module is high, which partly limits the use of this module. In this work, we propose the lightweight self-attention network (LSANet) for semantic segmentation. Specifically, the Lightweight Self-Attentive Module (LSAM) captures information using a hand-designed compact feature representation, and weighted fusion of position information. In the decoder structure, an improved up-sampling module is proposed. Compared with the bilinear upsampling, this method achieves better results in restoring image details. The experimental results on PASCAL VOC 2012, and Cityscapes datasets show the effectiveness of our method, which simplifies operations and improves performance.