Axial-DeepLab

Axial-DeepLab is a deep learning architecture for semantic segmentation tasks that was introduced in 2019. It is based on the popular DeepLab architecture, which uses atrous convolution (also known as dilated convolution) to capture multi-scale contextual information for improved segmentation accuracy.

Axial-DeepLab extends DeepLab by incorporating axial attention modules, which enable the network to focus on relevant features in the axial (i.e., spatial) dimensions of the input image. This allows the network to better handle objects with elongated or thin structures, such as roads, trees, and poles.

Axial-DeepLab also incorporates a multi-scale fusion module that combines features from different scales to improve the accuracy of the segmentation. Additionally, it uses a depth-wise separable convolutional block to reduce the number of parameters and improve computational efficiency.

Overall, Axial-DeepLab achieves state-of-the-art performance on several benchmark datasets for semantic segmentation, including PASCAL VOC and Cityscapes.