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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation

February 1, 2023

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Authors

Shaoyue Song

Beijing Jiaotong University


Hongkai Yu

University of Texas-Rio Grande Valley


Zhenjiang Miao

Beijing Jiaotong University


Jianwu Fang

Chang'an University


Kang Zheng

University of South Carolina


Cong Ma

Beijing Jiaotong University


Song Wang

University of South Carolina


DOI:

10.1609/aaai.v34i07.6879


Abstract:

Although there are many existing research works about the salient object detection (SOD) in RGB images, there are still many complex situations that regular RGB images cannot provide enough cues for the accurate SOD, such as the shadow effect, similar appearance between background and foreground, strong or insufficient illumination, etc. Because of the success of near-infrared spectrum in many computer vision tasks, we explore the multi-spectral SOD in the synchronized RGB images and near-infrared (NIR) images for the both simple and complex situations. We assume that the RGB SOD in the existing RGB image datasets could provide references for the multi-spectral SOD problem. In this paper, we first collect and will publicize a large multi-spectral dataset including 780 synchronized RGB and NIR image pairs for the multi-spectral SOD problem in the simple and complex situations. We model this research problem as an adversarial domain adaptation from the existing RGB image dataset (source domain) to the collected multi-spectral dataset (target domain). Experimental results show the effectiveness and accuracy of the proposed adversarial domain adaptation for the multi-spectral SOD.

Topics: AAAI

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HOW TO CITE:

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation Proceedings of the AAAI Conference on Artificial Intelligence (2020) 12023-12030.

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation AAAI 2020, 12023-12030.

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang (2020). Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 12023-12030.

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang. Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.12023-12030.

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang. 2020. Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation. "Proceedings of the AAAI Conference on Artificial Intelligence". 12023-12030.

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang. (2020) "Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation", Proceedings of the AAAI Conference on Artificial Intelligence, p.12023-12030

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang, "Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation", AAAI, p.12023-12030, 2020.

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang. "Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.12023-12030.

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang. "Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 12023-12030.

Shaoyue Song||Hongkai Yu||Zhenjiang Miao||Jianwu Fang||Kang Zheng||Cong Ma||Song Wang. Multi-Spectral Salient Object Detection by Adversarial Domain Adaptation. AAAI[Internet]. 2020[cited 2023]; 12023-12030.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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