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The large availability of depth sensors provides valuable complementary information for salient object detection (SOD) in RGBD images. However, due to the inherent difference between RGB and depth information, extracting features from the depth channel using ImageNet pre-trained backbone models and fusing them with RGB features directly are sub-optimal. In this paper, we utilize contrast prior, which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-baseddoi:10.1109/cvpr.2019.00405 dblp:conf/cvpr/ZhaoCFCLZ19 fatcat:jbf7qmti4rfavdojywljdhk5va