Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection

Jia-Xing Zhao, Yang Cao, Deng-Ping Fan, Ming-Ming Cheng, Xuan-Yi Li, Le Zhang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3927-3936

Abstract


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-based architecture to enhance the depth information. The enhanced depth cues are further integrated with RGB features for SOD, using a novel fluid pyramid integration, which can make better use of multi-scale cross-modal features. Comprehensive experiments on 5 challenging benchmark datasets demonstrate the superiority of the architecture CPFP over 9 state-of-the-art alternative methods.

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[bibtex]
@InProceedings{Zhao_2019_CVPR,
author = {Zhao, Jia-Xing and Cao, Yang and Fan, Deng-Ping and Cheng, Ming-Ming and Li, Xuan-Yi and Zhang, Le},
title = {Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}