Deep boundary-aware semantic image segmentation

While extensive research efforts have been made in semantic image segmentation, the state-of-the-art methods still suffer from blurry boundaries and mismatched objects due to the insufficient multiscale adaptability. In this paper, we propose a two-branch convolutional neural network (CNN) approach to capture the multiscale context and the boundary information with the two branches, respectively. To capture the multiscale context, we propose to embed self-attention mechanism to the atrous spatial pyramid pooling network. To capture the boundary information, we propose to fuse the low-level features in boundary feature extraction for refining the extracted boundaries via a feature fusion layer (FFL). With FFL, our method can improve the segmentation result with clearer boundaries. A new loss function is proposed which contains a segmentation loss and a boundary loss. Experiments show that our method can predict the boundaries of objects more clearly and have better performance for small-scale objects.