Multi-feature aggregation network for salient object detection-Reference-Cited by-同舟云学术

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Multi-feature aggregation network for salient object detection

Published:2022-07-21 Issue:4 Volume:17 Page:1043-1051
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Huang Hu,Liu Ping,Wang Yanzhao,Zhou Tongchi,Qu Boyang,Tao Aimin,Zhang Hao

Funder

National Natural Science Foundation of China

Zhongyuan Qianren Project

Key Scientific Research Project of Colleges and Universities in Henan Province

Basic research funds of Zhongyuan Unversity of technology

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-022-02310-3.pdf

Reference37 articles.

1. Gao, Y., Wang, M., Tao, D., et al.: 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)

2. Mahadevan, V., Vasconcelos, N.: Saliency-based discriminant tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1007–1013 (2009)

3. Li, B., Sun, Z., Tang, L., et al.: Two-B-real net: two-branch network for real-time salient object detection. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1662–1666 (2019)

4. Craye, C., Filliat, D., Goudou, J.F.: Environment exploration for object-based visual saliency learning. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2303–2309 (2016)

5. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

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