Knowledge Commons of Institute of Automation,CAS
Global-Guided Selective Context Network for Scene Parsing | |
Jiang, Jie1,2; Liu, Jing1,2; Fu, Jun1,2; Zhu, Xinxin1,2; Li, Zechao3; Lu, Hanqing1,2 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X |
2022-04-01 | |
卷号 | 33期号:4页码:1752-1764 |
通讯作者 | Liu, Jing(jliu@nlpr.ia.ac.cn) |
摘要 | Recent studies on semantic segmentation are exploiting contextual information to address the problem of inconsistent parsing prediction in big objects and ignorance in small objects. However, they utilize multilevel contextual information equally across pixels, overlooking those different pixels may demand different levels of context. Motivated by the above-mentioned intuition, we propose a novel global-guided selective context network (GSCNet) to adaptively select contextual information for improving scene parsing. Specifically, we introduce two global-guided modules, called global-guided global module (GGM) and global-guided local module (GLM), to, respectively, select global context (GC) and local context (LC) for pixels. When given an input feature map, GGM jointly employs the input feature map and its globally pooled feature to learn its global contextual demand based on which per-pixel GC is selected. While GLM adopts low-level feature from the adjacent stage as LC and synthetically models the input feature map, its globally pooled feature and LC to generate local contextual demand, based on which per-pixel LC is selected. Furthermore, we combine these two modules as a selective context block and import such SCBs in different levels of the network to propagate contextual information in a coarse-to-fine manner. Finally, we conduct extensive experiments to verify the effectiveness of our proposed model and achieve state-of-the-art performance on four challenging scene parsing data sets, i.e., Cityscapes, ADE20K, PASCAL Context, and COCO Stuff. Especially, GSCNet-101 obtains 82.6% on Cityscapes test set without using coarse data and 56.22% on ADE20K test set. |
关键词 | Semantics Task analysis Decoding Logic gates Image color analysis Fuses Feature extraction Attention mechanism (AM) contextual selection global guidance (GG) scene parsing |
DOI | 10.1109/TNNLS.2020.3043808 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61922086] ; National Natural Science Foundation of China[61872366] ; Beijing Natural Science Foundation[4192059] ; Beijing Natural Science Foundation[JQ20022] |
项目资助者 | National Natural Science Foundation of China ; Beijing Natural Science Foundation |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000778930100034 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48250 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Liu, Jing |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Nanjing Univ Sci & Technol, Sch Comp Sci, Nanjing 210094, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Jiang, Jie,Liu, Jing,Fu, Jun,et al. Global-Guided Selective Context Network for Scene Parsing[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022,33(4):1752-1764. |
APA | Jiang, Jie,Liu, Jing,Fu, Jun,Zhu, Xinxin,Li, Zechao,&Lu, Hanqing.(2022).Global-Guided Selective Context Network for Scene Parsing.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,33(4),1752-1764. |
MLA | Jiang, Jie,et al."Global-Guided Selective Context Network for Scene Parsing".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 33.4(2022):1752-1764. |
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