Object Segmentation by Mining Cross-Modal Semantics-Reference-Cited by-同舟云学术

Object Segmentation by Mining Cross-Modal Semantics

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Wu Zongwei¹^ORCID,Wang Jingjing²^ORCID,Zhou Zhuyun³^ORCID,An Zhaochong⁴^ORCID,Jiang Qiuping⁵^ORCID,Demonceaux Cédric³^ORCID,Sun Guolei⁴^ORCID,Timofte Radu⁶^ORCID

Affiliation:

1. Computer Vision Lab, CAIDAS & IFI, University of Wurzburg, Wurzburg, Germany

2. Anhui University of Science and Technology, Huainan, China

3. University of Burgundy, CNRS, ICB, Dijon, France

4. CVL, ETH Zurich, Zurich, Switzerland

5. Ningbo University, Ningbo, China

6. Computer Vision Lab, CAIDAS and IFI, University of Wurzburg, Wurzburg, Germany

Funder

Alexander von Humboldt Foundation

Conseil régional de Bourgogne-Franche-Comté

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611970

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4. Gang Chen , Feng Shao , Xiongli Chai , Hangwei Chen , Qiuping Jiang , Xiangchao Meng , and Yo-Sung Ho. 2022a. Modality-Induced Transfer-Fusion Network for RGB-D and RGB-T Salient Object Detection . IEEE TCSVT ( 2022 ). Gang Chen, Feng Shao, Xiongli Chai, Hangwei Chen, Qiuping Jiang, Xiangchao Meng, and Yo-Sung Ho. 2022a. Modality-Induced Transfer-Fusion Network for RGB-D and RGB-T Salient Object Detection. IEEE TCSVT (2022).

5. 3-D Convolutional Neural Networks for RGB-D Salient Object Detection and Beyond

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