Fast and Accurate Scene Parsing via Bi-Direction Alignment Networks

• Main Authors: Wu, Yanran, Li, Xiangtai, Shi, Chen, Tong, Yunhai, Hua, Yang, Song, Tao, Ma, Ruhui, Guan, Haibing
• Format: Conference Proceeding
• Language: English
• Subjects: Bidirectional Alignment Network; Bidirectional control; Conferences; Fast and Accurate Scene Parsing; Image segmentation; Logic gates; Semantics; Spatial resolution
• ISSN: 2381-8549
• DOI: 10.1109/ICIP42928.2021.9506720

In this paper, we propose an effective method for fast and accurate scene parsing called Bidirectional Alignment Network (BiAlignNet). Previously, one representative work BiSeNet [1] uses two different paths (Context Path and Spatial Path) to achieve balanced learning of semantics and details, respectively. However, the relationship between the two paths is not well explored. We argue that both paths can benefit each other in a complementary way. Motivated by this, we propose a novel network by aligning two-path information into each other through a learned flow field. To avoid the noise and semantic gaps, we introduce a Gated Flow Alignment Module to align both features in a bidirectional way. Moreover, to make the Spatial Path learn more detailed information, we present an edge-guided hard pixel mining loss to supervise the aligned learning process. Our network achieves 80.1parcent and 78.5parcent mIoU in validation and test set of Cityscapes while running at 30 FPS with full resolution inputs. Code and models will be available at https://github.com/jojacola/BiAlignNet.