A novel decoder based on Bayesian rules for task‐driven object segmentation-Reference-Cited by-同舟云学术

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A novel decoder based on Bayesian rules for task‐driven object segmentation

Published:2022-11-08 Issue:3 Volume:17 Page:832-848
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET Image Processing

Author:

Cai Yuxiang¹²,Yu Yuanlong³^ORCID,Jiang Weijie³^ORCID,Chen Rong³,Zheng Weitao²,Wu Xi²,Su Renjie²

Affiliation:

1. School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

2. Technology Department State Grid Fujian Information & Telecommunication Company Fuzhou China

3. College of Computer and Data Science Fuzhou University Fuzhou China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12676

Reference50 articles.

1. Wang W. Lai Q. Fu H. Shen J. Ling H.:Salient object detection in the deep learning era: an in‐depth survey. arXiv:190409146 (2019)

2. Wang L. Lu H. Ruan X. Yang M.H.:Deep networks for saliency detection via local estimation and global search. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.3183–3192.IEEE Piscataway NJ(2015)

3. Zhang P. Wang D. Lu H. Wang H. Ruan X.:Amulet: aggregating multi‐level convolutional features for salient object detection. In:Proceedings of the IEEE International Conference on Computer Vision pp.202–211.IEEE Piscataway NJ(2017)

4. Zhang P. Wang D. Lu H. Wang H. Yin B.:Learning uncertain convolutional features for accurate saliency detection. In:Proceedings of the IEEE International Conference on Computer Vision pp.212–221.IEEE Piscataway NJ(2017.)

5. Liu N. Han J. Yang M.H.:PiCANet: learning pixel‐wise contextual attention for saliency detection. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp.3089–3098.IEEE Piscataway NJ(2018)

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