  Compared with other semantic segmentation tasks, portrait segmentation
requires both higher precision and faster inference speed. However, this
problem has not been well studied in previous works. In this paper, we propose
a lightweight network architecture, called Boundary-Aware Network (BANet) which
selectively extracts detail information in boundary area to make high-quality
segmentation output with real-time( >25FPS) speed. In addition, we design a new
loss function called refine loss which supervises the network with image level
gradient information. Our model is able to produce finer segmentation results
which has richer details than annotations.
