Details for method 'DSANet: Dilated Spatial Attention for Real-time Semantic Segmentation in Urban Street Scenes'
|name||DSANet: Dilated Spatial Attention for Real-time Semantic Segmentation in Urban Street Scenes|
|challenge||pixel-level semantic labeling|
|details||we present computationally efficient network named DSANet, which follows a two-branch strategy to tackle the problem of real-time semantic segmentation in urban scenes. We first design a Context branch, which employs Depth-wise Asymmetric ShuffleNet DAS as main building block to acquire sufficient receptive fields. In addition, we propose a dual attention module consisting of dilated spatial attention and channel attention to make full use of the multi-level feature maps simultaneously, which helps predict the pixel-wise labels in each stage. Meanwhile, Spatial Encoding Network is used to enhance semantic information by preserving the spatial details. Finally, to better combine context information and spatial information, we introduce a Simple Feature Fusion Module to combine the features from the two branches.|
|project page / code|
|used Cityscapes data||fine annotations|
|used external data|
|submission date||February, 2021|