Shaoyue Song, Hongkai Yu, Zhenjiang Miao, Jianwu Fang, Kang Zheng, Cong Ma, and Song Wang
Abstract
Salient Object Detection (SOD) plays an important
role in many image-related multimedia applications. Although
there are many existing research works about the salient object
detection in traditional RGB (visible-light spectrum) 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 mainly model this
research problem as a deep learning based domain adaptation
from the traditional RGB image data (source domain) to the
multi-spectral data (target domain), and an adversarial deep
domain adaptation model is proposed. We first collect and will
publicize a large multi-spectral dataset, RGBN-SOD dataset,
including 780 synchronized RGB and NIR image pairs for the
multi-spectral SOD problem in the simple and complex situations.
Experimental results show the effectiveness and accuracy of the
proposed deep domain adaptation for the multi-spectral SOD.
Publication
Shaoyue Song, Hongkai Yu, Zhenjiang Miao, Jianwu Fang, Kang Zheng, Cong Ma, Song Wang. Multi-spectral Salient Object Detection by Adversarial Domain Adaptation,
AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020. [PDF]
Shaoyue Song, Zhenjiang Miao, Hongkai Yu, Jianwu Fang, Kang Zheng, Cong Ma, Song Wang. Deep Domain Adaptation Based Multi-spectral Salient Object Detection,
IEEE Transactions on Multimedia, 2020. [PDF]