# Siamese Network for RGB-D Salient Object Detection and Beyond

arXiv: https://arxiv.org/abs/2008.12134 · HTML: https://mlsanity.com/paper/2008.12134/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 30 of 67)

- [Concealed Object Detection](https://mlsanity.com/paper/2102.10274/) (2021)
- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection](https://mlsanity.com/paper/2112.02363/) (2021)
- [Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network](https://mlsanity.com/paper/2111.03216/) (2021)
- [CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection](https://mlsanity.com/paper/2210.02843/) (2022)
- [Does Thermal Really Always Matter for RGB-T Salient Object Detection?](https://mlsanity.com/paper/2210.04266/) (2022)
- [RGB-D Salient Object Detection via 3D Convolutional Neural Networks](https://mlsanity.com/paper/2101.10241/) (2021)
- [Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection](https://mlsanity.com/paper/2107.01779/) (2021)
- [HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness](https://mlsanity.com/paper/2301.07405/) (2023)
- [Effectiveness Assessment of Recent Large Vision-Language Models](https://mlsanity.com/paper/2403.04306/) (2024)
- [HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection](https://mlsanity.com/paper/2301.03036/) (2023)
- [Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2308.08930/) (2023)
- [Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning](https://mlsanity.com/paper/2312.00360/) (2023)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [Generative Transformer for Accurate and Reliable Salient Object Detection](https://mlsanity.com/paper/2104.10127/) (2021)
- [UniRGB-IR: A Unified Framework for Visible-Infrared Semantic Tasks via Adapter Tuning](https://mlsanity.com/paper/2404.17360/) (2024)
- [Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation](https://mlsanity.com/paper/2101.09014/) (2021)
- [Pixel Difference Convolutional Network for RGB-D Semantic Segmentation](https://mlsanity.com/paper/2302.11951/) (2023)
- [CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation](https://mlsanity.com/paper/2307.08098/) (2023)
- [mEBAL2 Database and Benchmark: Image-based Multispectral Eyeblink Detection](https://mlsanity.com/paper/2309.07880/) (2023)
- [Salient Object Detection in RGB-D Videos](https://mlsanity.com/paper/2310.15482/) (2023)
- [High-Resolution Cloud Detection Network](https://mlsanity.com/paper/2407.07365/) (2024)
- [M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision](https://mlsanity.com/paper/2507.16318/) (2025)
- [Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance](https://mlsanity.com/paper/2408.15063/) (2024)
- [SSFam: Scribble Supervised Salient Object Detection Family](https://mlsanity.com/paper/2409.04817/) (2024)
- [Lightweight RGB-D Salient Object Detection from a Speed-Accuracy Tradeoff Perspective](https://mlsanity.com/paper/2505.04758/) (2025)
- [Multispectral State-Space Feature Fusion: Bridging Shared and Cross-Parametric Interactions for Object Detection](https://mlsanity.com/paper/2507.14643/) (2025)
- [MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention](https://mlsanity.com/paper/2112.01177/) (2021)
- [Visual Object Tracking on Multi-modal RGB-D Videos: A Review](https://mlsanity.com/paper/2201.09207/) (2022)
- [DFTR: Depth-supervised Fusion Transformer for Salient Object Detection](https://mlsanity.com/paper/2203.06429/) (2022)

## Abstract (arXiv)

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately designed training process. Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture. In this paper, we propose two effective components: joint learning (JL), and densely cooperative fusion (DCF). The JL module provides robust saliency feature learning by exploiting cross-modal commonality via a Siamese network, while the DCF module is introduced for complementary feature discovery. Comprehensive experiments using five popular metrics show that the designed framework yields a robust RGB-D saliency detector with good generalization. As a result, JL-DCF significantly advances the state-of-the-art models by an average of ~2.0% (max F-measure) across seven challenging datasets. In addition, we show that JL-DCF is readily applicable to other related multi-modal detection tasks, including RGB-T (thermal infrared) SOD and video SOD, achieving comparable or even better performance against state-of-the-art methods. We also link JL-DCF to the RGB-D semantic segmentation field, showing its capability of outperforming several semantic segmentation models on the task of RGB-D SOD. These facts further confirm that the proposed framework could offer a potential solution for various applications and provide more insight into the cross-modal complementarity task.