Papers › Select, Supplement and Focus for RGB-D Saliency Detection

Select, Supplement and Focus for RGB-D Saliency Detection

1 Jun 2020CVPR 2020 6archive 2025-07-28

Miao Zhang, Weisong Ren, Yongri Piao, Zhengkun Rong, Huchuan Lu

Depth data containing a preponderance of discriminative power in location have been proven beneficial for accurate saliency prediction. However, RGB-D saliency detection methods are also negatively influenced by randomly distributed erroneous or missing regions on the depth map or along the object boundaries. This offers the possibility of achieving more effective inference by well designed models. In this paper, we propose a new framework for accurate RGB-D saliency detection taking account of local and global complementarities from two modalities. This is achieved by designing a complimentary interaction model discriminative enough to simultaneously select useful representation from RGB and depth data, and meanwhile to refine the object boundaries. Moreover, we proposed a compensation-aware loss to further process the information not being considered in the complimentary interaction model, leading to improvement of the generalization ability for challenging scenes. Experiments on six public datasets show that our method outperforms18state-of-the-art methods.

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OIPLab-DUT/CVPR_SSF-RGBD pytorch report

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Tasks

RGB-D Salient Object Detection Saliency Detection Saliency Prediction Thermal Image Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
RGB-D Salient Object Detection	NJU2K	SSF	Average MAE	0.043	#16 of 27	Archive leaderboard	report
RGB-D Salient Object Detection	NJU2K	SSF	S-Measure	89.9	#16 of 27	Archive leaderboard	report
Thermal Image Segmentation	RGB-T-Glass-Segmentation	SSF	MAE	0.097	#18 of 22	Archive leaderboard	report

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