# Depth Quality Aware Salient Object Detection

arXiv: https://arxiv.org/abs/2008.04159 · HTML: https://mlsanity.com/paper/2008.04159/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 17 of 17)

- [TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network](https://mlsanity.com/paper/2108.03990/) (2021)
- [CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection](https://mlsanity.com/paper/2210.02843/) (2022)
- [Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection](https://mlsanity.com/paper/2107.01779/) (2021)
- [Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection](https://mlsanity.com/paper/2205.07179/) (2022)
- [Position-Aware Relation Learning for RGB-Thermal Salient Object Detection](https://mlsanity.com/paper/2209.10158/) (2022)
- [A Comprehensive Survey on Video Saliency Detection with Auditory Information: the Audio-visual Consistency Perceptual is the Key!](https://mlsanity.com/paper/2206.13390/) (2022)
- [Quality-aware Selective Fusion Network for V-D-T Salient Object Detection](https://mlsanity.com/paper/2405.07655/) (2024)
- [Automotive Object Detection via Learning Sparse Events by Spiking Neurons](https://mlsanity.com/paper/2307.12900/) (2023)
- [Self-Distribution Binary Neural Networks](https://mlsanity.com/paper/2103.02394/) (2021)
- [A Novel Long-term Iterative Mining Scheme for Video Salient Object Detection](https://mlsanity.com/paper/2206.09564/) (2022)
- [Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2206.03105/) (2022)
- [CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection](https://mlsanity.com/paper/2407.06780/) (2024)
- [Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection](https://mlsanity.com/paper/2409.03236/) (2024)
- [Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection](https://mlsanity.com/paper/2208.03918/) (2022)
- [HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2307.00954/) (2023)
- [Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection](https://mlsanity.com/paper/2501.01648/) (2025)
- [SSNet: Saliency Prior and State Space Model-based Network for Salient Object Detection in RGB-D Images](https://mlsanity.com/paper/2503.02270/) (2025)

## Abstract (arXiv)

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.