Multi Information Fusion Network for Saliency Quality Assessment-Reference-Cited by-同舟云学术

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Multi Information Fusion Network for Saliency Quality Assessment

Published:2019-05-01 Issue:5 Volume:E102.D Page:1111-1114
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

TAN Kai¹,WU Qingbo¹,MENG Fanman¹,XU Linfeng¹

Affiliation:

1. School of Information and Communication Engineering, University of Electronic Science and Technology of China

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E102.D/5/E102.D_2019EDL8002/_pdf

Reference18 articles.

1. [1] Q. Wu, H. Li, F. Meng, K.N. Ngan, B. Luo, C. Huang, and B. Zeng, “Blind image quality assessment based on multichannel feature fusion and label transfer,” IEEE Trans. Circuits Syst. Video Technol., vol.26, no.3, pp.425-440, March 2016. 10.1109/tcsvt.2015.2412773

2. [2] Q. Zhou, J. Cheng, H. Lu, Y. Fan, S. Zhang, X. Wu, B. Zheng, W. Ou, and L.J. Latecki, “Learning adaptive contrast combinations for visual saliency detection,” Multimedia Tools and Applications, pp.1-29, 2018. 10.1007/s11042-018-6770-2

3. [3] L. Tang, Q. Wu, W. Li, and Y. Liu, “Deep saliency quality assessment network with joint metric,” IEEE Access, vol.6, pp.913-924, 2018. 10.1109/access.2017.2776344

4. [4] L. Mai and F. Liu, “Comparing salient object detection results without ground truth,” European Conference on Computer Vision, Lecture Notes in Computer Science, vol.8691, pp.76-91, Springer, 2014. 10.1007/978-3-319-10578-9_6

5. [5] C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang, “Saliency detection via graph-based manifold ranking,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.3166-3173, 2013. 10.1109/cvpr.2013.407

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1. No-reference shadow detection quality assessment via reference learning and multi-mode exploring;Computers & Graphics;2023-11

2. Local Interaction and Global Guidance Based Low and High-level Feature Fusion for RGB-D Fixation Prediction;2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2021-12

3. CascNet: No-reference saliency quality assessment with cascaded applicability sorting and comparing network;Neurocomputing;2021-02

4. DrlNet: Blind object proposal quality assessment with discriminative response learning;Digital Signal Processing;2020-11

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