Multi-modal Weights Sharing and Hierarchical Feature Fusion for RGBD Salient Object Detection

Fen Xiao, Bin Li, Yimu Peng, Chunhong Cao, Kai Hu, Xieping Gao
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Salient object detection (SOD) aims to identify and locate the most attractive regions in an image, which has been widely used in various vision tasks. Recent years, with the development of RGBD sensor technology, depth information of scenes becomes available for image understanding. In this paper, we systematically investigate and evaluate on how to integrate depth cues in a pre-trained deep network and learn informative features for SOD. First, we propose a CNN-based cross-modal transfer
more &raquo; ... ing, which learn knowledge from sufficient labeled RGB salient object datasets and guide the depth domain feature extraction. Then we design a feature fusion module to fuse the complementary features in a hierarchical manner. At last, the final saliency map is obtained by integrating multi-scale information step by step. Extensive experiments on five popular RGBD benchmark datasets demonstrate that our proposed approach achieves significant improvements and outperforms the state-of-the-art methods. INDEX TERMS RGBD, salient object detection, complementary feature extraction, hierarchical fusion. 26602 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2971509">doi:10.1109/access.2020.2971509</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aze3ddzokjcy3pn764iffcibsm">fatcat:aze3ddzokjcy3pn764iffcibsm</a> </span>
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