Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048x1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.
@article{arxiv.1808.00897,
title = {BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation},
author = {Changqian Yu and Jingbo Wang and Chao Peng and Changxin Gao and Gang Yu and Nong Sang},
journal= {arXiv preprint arXiv:1808.00897},
year = {2018}
}
Comments
Accepted to ECCV 2018. 17 pages, 4 figures, 9 tables