CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
An Efficient Sampling-Based Attention Network for Semantic Segmentation
He, Xingjian1,2; Liu, Jing1,2; Wang, Weining2; Lu, Hanqing1,2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2022
Volume31Pages:2850-2863
Corresponding AuthorLiu, Jing(jliu@nlpr.ia.ac.cn)
AbstractSelf-attention is widely explored to model long-range dependencies in semantic segmentation. However, this operation computes pair-wise relationships between the query point and all other points, leading to prohibitive complexity. In this paper, we propose an efficient Sampling-based Attention Network which combines a novel sample method with an attention mechanism for semantic segmentation. Specifically, we design a Stochastic Sampling-based Attention Module (SSAM) to capture the relationships between the query point and a stochastic sampled representative subset from a global perspective, where the sampled subset is selected by a Stochastic Sampling Module. Compared to self-attention, our SSAM achieves comparable segmentation performance while significantly reducing computational redundancy. In addition, with the observation that not all pixels are interested in the contextual information, we design a Deterministic Sampling-based Attention Module (DSAM) to sample features from a local region for obtaining the detailed information. Extensive experiments demonstrate that our proposed method can compete or perform favorably against the state-of-the-art methods on the Cityscapes, ADE20K, COCO Stuff, and PASCAL Context datasets.
KeywordStochastic processes Sampling methods Semantics Image segmentation Computational complexity Pattern recognition Convolution Semantic segmentation stochastic sampling-based attention deterministic sampling-based attention
DOI10.1109/TIP.2022.3162101
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2020AAA0106400] ; National Natural Science Foundation of China[61922086] ; National Natural Science Foundation of China[61872366] ; National Natural Science Foundation of China[U21B2043]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000778905000007
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification图像视频处理与分析
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48290
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorLiu, Jing
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
He, Xingjian,Liu, Jing,Wang, Weining,et al. An Efficient Sampling-Based Attention Network for Semantic Segmentation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:2850-2863.
APA He, Xingjian,Liu, Jing,Wang, Weining,&Lu, Hanqing.(2022).An Efficient Sampling-Based Attention Network for Semantic Segmentation.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,2850-2863.
MLA He, Xingjian,et al."An Efficient Sampling-Based Attention Network for Semantic Segmentation".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):2850-2863.
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