Deep Co-Saliency Detection via Stacked Autoencoder-Enabled Fusion and Self-Trained CNNs-Reference-Cited by-同舟云学术

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Deep Co-Saliency Detection via Stacked Autoencoder-Enabled Fusion and Self-Trained CNNs

Published:2020-04 Issue:4 Volume:22 Page:1016-1031
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Tsai Chung-Chi^ORCID,Hsu Kuang-Jui^ORCID,Lin Yen-Yu^ORCID,Qian Xiaoning^ORCID,Chuang Yung-Yu^ORCID

Funder

Ministry of Science and Technology

MOST Joint Research Center for AI Technology and All Vista Healthcare

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/6046/9046171/8809285-aam.pdf

Cited by 13 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Study and Analysis of Visual Saliency Applications Using Graph Neural Networks;Concepts and Techniques of Graph Neural Networks;2023-03-03

2. Global-and-Local Collaborative Learning for Co-Salient Object Detection;IEEE Transactions on Cybernetics;2023-03

3. Modified ResNet50 model and semantic segmentation based image co-saliency detection;Journal of Information and Optimization Sciences;2023

4. Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection;2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2022-06

5. Image Co-Saliency Detection and Instance Co-Segmentation Using Attention Graph Clustering Based Graph Convolutional Network;IEEE Transactions on Multimedia;2022

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