Scribble-attention hierarchical network for weakly supervised salient object detection in optical remote sensing images-Reference-Cited by-同舟云学术

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Scribble-attention hierarchical network for weakly supervised salient object detection in optical remote sensing images

Published:2022-10-05 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Ma Lei,Luo Xinyi,Hong Hanyu,Zhang Yaozong,Wang Lei,Wu Jinmeng

Funder

National Natural Science Foundation of China

Startup Foundation of Wuhan Institute of Technology

Guiding Scientific Research Project of Hubei Provincial Department of Education

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-04014-0.pdf

Reference60 articles.

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2. Bolme DS, Beveridge JR, Draper BA, Lui YM (2010) Visual object tracking using adaptive correlation filters. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2544–2550. https://doi.org/10.1109/CVPR.2010.5539960

3. Cho S, Jang H, Tan JW, Jeong W (2021) Deepscribble: interactive pathology image segmentation using deep neural networks with scribbles. In: IEEE International symposium on biomedical imaging, pp 761–765. https://doi.org/10.1109/ISBI48211.2021.9434105

4. Cong R, Zhang Y, Fang L, Li J, Zhao Y, Kwong S (2022) Rrnet: relational reasoning network with parallel multiscale attention for salient object detection in optical remote sensing images. IEEE Trans Geosci Remote Sens 60:1–11. https://doi.org/10.1109/TGRS.2021.3123984

5. Fan D, Cheng M, Liu Y, Li T, Borji A (2017) Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE international conference on computer vision, pp 4558–4567. https://doi.org/10.1109/ICCV.2017.487

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