Research on the Application of Unet with Convolutional Block Attention Module to Semantic Segmentation Task

Attention mechanism can focus on important features and suppress unnecessary features, to increase the representational power of the network, which plays an important role in visual tasks such as semantic segmentation. To this end, the UNET of Convolutional Block Attention Module (CBAM) is applied in semantic segmentation tasks to improve the performance of convolutional neural networks. The channel attention adopts maximum pooling and average pooling, and for the spatial attention, a smaller convolution kernel is proposed to reduce computation and loss of important features. Through experiments, the introduction of CBAM has a improvement in semantic segmentation tasks increasing the Validation Dice from 0.98 to 0.9871 in the Kaggle Carvana image segmentation dataset.