{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bisenet-bilateral-segmentation-network-for","title":"BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation","arxiv_id":"1808.00897","date":"2018-08-02","proceeding":"ECCV 2018 9","authors":["Changqian Yu","Jingbo Wang","Chao Peng","Changxin Gao","Gang Yu","Nong Sang"],"abstract":"Semantic segmentation requires both rich spatial information and sizeable\nreceptive field. However, modern approaches usually compromise spatial\nresolution to achieve real-time inference speed, which leads to poor\nperformance. In this paper, we address this dilemma with a novel Bilateral\nSegmentation Network (BiSeNet). We first design a Spatial Path with a small\nstride to preserve the spatial information and generate high-resolution\nfeatures. Meanwhile, a Context Path with a fast downsampling strategy is\nemployed to obtain sufficient receptive field. On top of the two paths, we\nintroduce a new Feature Fusion Module to combine features efficiently. The\nproposed architecture makes a right balance between the speed and segmentation\nperformance on Cityscapes, CamVid, and COCO-Stuff datasets. 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