Specificity-preserving RGB-D saliency detection
CC BY
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Springer
2023
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Online Access: | https://link.springer.com/article/10.1007/s41095-022-0268-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7314 |
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oai:localhost:PNK-73142023-03-30T02:51:02Z Specificity-preserving RGB-D saliency detection Tao, Zhou Deng-Ping, Fan Geng, Chen SOD RGB CC BY Salient object detection (SOD) in RGB and depth images has attracted increasing research interest. Existing RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities, while few methods explicitly consider how to preserve modality-specific characteristics. In this study, we propose a novel framework, the specificity-preserving network (SPNet), which improves SOD performance by exploring both the shared information and modality-specific properties. 2023-03-30T02:51:02Z 2023-03-30T02:51:02Z 2023 Book https://link.springer.com/article/10.1007/s41095-022-0268-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7314 en application/pdf Springer |
institution |
Digital Phenikaa |
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Digital Phenikaa |
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English |
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SOD RGB |
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SOD RGB Tao, Zhou Deng-Ping, Fan Geng, Chen Specificity-preserving RGB-D saliency detection |
description |
CC BY |
format |
Book |
author |
Tao, Zhou Deng-Ping, Fan Geng, Chen |
author_facet |
Tao, Zhou Deng-Ping, Fan Geng, Chen |
author_sort |
Tao, Zhou |
title |
Specificity-preserving RGB-D saliency detection |
title_short |
Specificity-preserving RGB-D saliency detection |
title_full |
Specificity-preserving RGB-D saliency detection |
title_fullStr |
Specificity-preserving RGB-D saliency detection |
title_full_unstemmed |
Specificity-preserving RGB-D saliency detection |
title_sort |
specificity-preserving rgb-d saliency detection |
publisher |
Springer |
publishDate |
2023 |
url |
https://link.springer.com/article/10.1007/s41095-022-0268-6 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7314 |
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1761821912332238848 |
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8.891145 |