Depth Quality Aware Salient Object Detection

Abstract

The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D). The D quality usually varies from scene to scene, while the SOTA bi-stream approaches are depth quality unaware, which easily result in substantial difficulties in achieving complementary fusion status between RGB and D, leading to poor fusion results in facing of low-quality D. Thus, this paper attempts to integrate a novel depth quality aware subnet into the classic bi-stream structure, aiming to assess the depth quality before conducting the selective RGB-D fusion. Compared with the SOTA bi-stream methods, the major highlight of our method is its ability to lessen the importance of those low-quality, no-contribution, or even negative-contribution D regions during the RGB-D fusion, achieving a much improved complementary status between RGB and D.

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Cited by in corpus (8)

• RGB-D Salient Object Detection: A Survey
• CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
• TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network
• Position-Aware Relation Learning for RGB-Thermal Salient Object Detection
• Quality-aware Selective Fusion Network for V-D-T Salient Object Detection
• Self-Distribution Binary Neural Networks
• Automotive Object Detection via Learning Sparse Events by Spiking Neurons
• Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection