An Efficient Sampling-Based Attention Network for Semantic Segmentation

doi:10.1109/TIP.2022.3162101

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 图像与视频分析团队

	An Efficient Sampling-Based Attention Network for Semantic Segmentation
	He, Xingjian 1,2; Liu, Jing 1,2; Wang, Weining 2; Lu, Hanqing 1,2
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2022
卷号	31 页码:2850-2863
关键词	Stochastic processes Sampling methods Semantics Image segmentation Computational complexity Pattern recognition Convolution Semantic segmentation stochastic sampling-based attention deterministic sampling-based attention
ISSN号	1057-7149
DOI	10.1109/TIP.2022.3162101
通讯作者	Liu, Jing(jliu@nlpr.ia.ac.cn)
英文摘要	Self-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.
资助项目	National 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]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000778905000007
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48289]
专题	自动化研究所_模式识别国家重点实验室_图像与视频分析团队
通讯作者	Liu, Jing
作者单位	1.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
推荐引用方式 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.