Bilateral attention network for semantic segmentation

Enhancing network feature representation capabilities and reducing the loss of image details have become the focus of semantic segmentation task. This work proposes the bilateral attention network for semantic segmentation. The authors embed two attention modules in the encoder and decoder structures . Specifically, high‐level features of the encoder structure integrate all channel maps through dense channel relationships learned by the channel correlation coefficient attention module. The positively correlated channels promote each other, and the negatively correlated channels suppress each other. In the decoder structure, low‐level features selectively emphasize the edge detail information in the feature map through the position attention module. The feature expression of semantic segmentation is improved by feature fusion of the two attention modules to obtain more accurate segmentation results . Finally, to verify the effectiveness of the model, the authors conduct experiments on the PASCAL VOC 2012 and Cityscapes scene analysis benchmark data sets and achieve a mean intersection‐over‐union of 74.92% and 66.63%, respectively.