# Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation

Source: [Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation](<https://www.sota2.com/research/paper/68fa654d1e209131cea09751>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:56:48.736Z

Authors: Xi Chen, Donglian Qi, Jianxin Shen

Published: 2019-01-12T07:19:27.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1901.03814>)

[PDF](<https://arxiv.org/pdf/1901.03814.pdf>)