Global attention network for collaborative saliency detection

doi:10.1007/s13042-022-01531-9

CORC > 兰州理工大学 > 兰州理工大学

	Global attention network for collaborative saliency detection
	Li, Ce; Xuan, Shuxing; Liu, Fenghua; Chang, Enbing; Wu, Hailei
刊名	International Journal of Machine Learning and Cybernetics
	2022
关键词	Image processing Attention Co saliencies Collaborative correlation Feature enhancement Global context Global informations Individual features Saliency detection Single images Spatial attention
ISSN号	1868-8071
DOI	10.1007/s13042-022-01531-9
英文摘要	Collaborative saliency (cosaliency) detection aims to identify common and saliency objects or regions in a set of related images. The major challenge to address is how to extract useful information on single images and image groups to express collaborative saliency cues. In this paper, we propose a global attention network for cosaliency detection to extract individual features from the feature enhancement module (FEM). Then to capture useful global information, the global information module (GIM) is applied to all individual features to obtain individual cues, and finally, group collaborative cues are obtained by the collaboration correlation module (CCM). Specifically, the channel attention module and spatial attention module are plugged into the convolution feature network. To increase global context information, we perform global information module (GIM) on the preprocessed features and embed nonlocal modules in the backbone network and adopt global average pooling to extract global semantic representation vector as individual cues. Then, we build a collaborative correlation module (CCM) to extract collaborative and consistent information by calculating the correlation between the individual features of the input image and individual cues in the collaborative correlation module. We evaluate our method on two cosaliency detection benchmark datasets (CoSal2015, iCoSeg). Extensive experiments demonstrate the effectiveness of the proposed model, in most cases our method exceeds the state-of-the-art methods. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
语种	英语
出版者	Springer Science and Business Media Deutschland GmbH
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/159086]
专题	兰州理工大学
作者单位	College of Electrical and Information Engineering, Lanzhou University of Technology, Gansu, Lanzhou, China
推荐引用方式 GB/T 7714	Li, Ce,Xuan, Shuxing,Liu, Fenghua,et al. Global attention network for collaborative saliency detection[J]. International Journal of Machine Learning and Cybernetics,2022.
APA	Li, Ce,Xuan, Shuxing,Liu, Fenghua,Chang, Enbing,&Wu, Hailei.(2022).Global attention network for collaborative saliency detection.International Journal of Machine Learning and Cybernetics.
MLA	Li, Ce,et al."Global attention network for collaborative saliency detection".International Journal of Machine Learning and Cybernetics (2022).