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Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images

IEEE Transactions on Image Processing, 2022
Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques.
Yueting Zhuang, Yi Yang, Ping Li
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Gender Recognition on RGB-D Image

2020 IEEE International Conference on Image Processing (ICIP), 2020
In this paper, we propose a deep-learning approach for human gender classification on RGB-D images. Unlike most of the existing methods, which use hand-crafted features from the human face, we exploit local information from the head and global information from the whole body to classify people’s gender.
Xiaoxiong Zhang 0001, Sajid Javed, Ahmad Obeid 0001, Jorge Dias 0001, Naoufel Werghi   +4 more
openaire   +2 more sources

Visual Saliency Detection for RGB-D Images Under a Bayesian Framework

IPSJ Transactions on Computer Vision and Applications, 2018
In this paper, we propose a saliency detection model for RGB-D images based on the deep features of RGB images and depth images within a Bayesian framework. By analysing 3D saliency in the case of RGB images and depth images, the class-conditional mutual
Songtao Wang
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