GPNet: Gated pyramid network for semantic segmentation. (July 2021)

• Record Type: Journal Article
• Authors: Zhang, Yu; Sun, Xin; Dong, Junyu; Chen, Changrui; Lv, Qingxuan
• Is Part Of: Pattern recognition. Volume 115(2021)
• Publication Date: 2021-07
• Subjects: Deep learning -- Semantic segmentation -- Context embedding -- Gated mechanism -- Attention; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.107940
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Propose a gated pyramid module to incorporate both low-level and high-level features. Apply gated path to filter the useful feature and obtain robust semantic context. Propose the cross-layer attention module to further exploit context from shallow layers. Refine the noisy upsampled features and retain the spatial context by using cross-layer attentions. Abstract: Semantic segmentation is a challenging task which requires both solid unanimous global context and rich spatial information. Recent methods ignore adaptively capturing of valid feature. The lack of useful multi-scale information filtering hinders further explicit feature generation. In this paper, we develop a novel network named GPNet, which can densely capture and filter the multi-scale information in a gated and pair-wise manner. Specifically, a Gated Pyramid Module (GPM) is designed to incorporate dense and growing receptive fields from both low-level and high-level features. In GPM we build a gated path to select useful context among multi-scale information. Moreover, a Cross-Layer Attention Module (CLAM) is proposed to reuse the context information from shallow layers to guide the deep features. Comprehensive experimental evaluations are conducted on popular semantic segmentation benchmarks including Cityscapes and ADE20K. Our GPNet achieves the mIoU score of 82.5% and 45.81% on Cityscapes test set and ADE20K validation set, respectively, which are the new state-of-the-art results using ResNet-101 as the backbone.