# RGB-D Salient Object Detection with Cross-Modality Modulation and Selection

arXiv: https://arxiv.org/abs/2007.07051 · HTML: https://mlsanity.com/paper/2007.07051/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 30 of 50)

- [Visual Saliency Transformer](https://mlsanity.com/paper/2104.12099/) (2021)
- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection](https://mlsanity.com/paper/2204.05585/) (2022)
- [Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion](https://mlsanity.com/paper/2103.11832/) (2021)
- [RGB-D Saliency Detection via Cascaded Mutual Information Minimization](https://mlsanity.com/paper/2109.07246/) (2021)
- [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)
- [Does Thermal Really Always Matter for RGB-T Salient Object Detection?](https://mlsanity.com/paper/2210.04266/) (2022)
- [Frequency Perception Network for Camouflaged Object Detection](https://mlsanity.com/paper/2308.08924/) (2023)
- [RGB-D Salient Object Detection via 3D Convolutional Neural Networks](https://mlsanity.com/paper/2101.10241/) (2021)
- [UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space](https://mlsanity.com/paper/2103.07138/) (2021)
- [Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2108.01971/) (2021)
- [RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2110.14223/) (2021)
- [Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection](https://mlsanity.com/paper/2107.01779/) (2021)
- [BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation](https://mlsanity.com/paper/2107.12541/) (2021)
- [HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness](https://mlsanity.com/paper/2301.07405/) (2023)
- [VST++: Efficient and Stronger Visual Saliency Transformer](https://mlsanity.com/paper/2310.11725/) (2023)
- [Glass Segmentation with RGB-Thermal Image Pairs](https://mlsanity.com/paper/2204.05453/) (2022)
- [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)
- [Global-and-Local Collaborative Learning for Co-Salient Object Detection](https://mlsanity.com/paper/2204.08917/) (2022)
- [Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution](https://mlsanity.com/paper/2306.00386/) (2023)
- [Generative Transformer for Accurate and Reliable Salient Object Detection](https://mlsanity.com/paper/2104.10127/) (2021)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection](https://mlsanity.com/paper/2104.01784/) (2021)
- [RGB-D Salient Object Detection with Ubiquitous Target Awareness](https://mlsanity.com/paper/2109.03425/) (2021)
- [Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection](https://mlsanity.com/paper/2205.07179/) (2022)
- [Robust RGB-D Fusion for Saliency Detection](https://mlsanity.com/paper/2208.01762/) (2022)
- [PSNet: Parallel Symmetric Network for Video Salient Object Detection](https://mlsanity.com/paper/2210.05912/) (2022)
- [SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection](https://mlsanity.com/paper/2308.08935/) (2023)

## Abstract (arXiv)

We present an effective method to progressively integrate and refine the cross-modality complementarities for RGB-D salient object detection (SOD). The proposed network mainly solves two challenging issues: 1) how to effectively integrate the complementary information from RGB image and its corresponding depth map, and 2) how to adaptively select more saliency-related features. First, we propose a cross-modality feature modulation (cmFM) module to enhance feature representations by taking the depth features as prior, which models the complementary relations of RGB-D data. Second, we propose an adaptive feature selection (AFS) module to select saliency-related features and suppress the inferior ones. The AFS module exploits multi-modality spatial feature fusion with the self-modality and cross-modality interdependencies of channel features are considered. Third, we employ a saliency-guided position-edge attention (sg-PEA) module to encourage our network to focus more on saliency-related regions. The above modules as a whole, called cmMS block, facilitates the refinement of saliency features in a coarse-to-fine fashion. Coupled with a bottom-up inference, the refined saliency features enable accurate and edge-preserving SOD. Extensive experiments demonstrate that our network outperforms state-of-the-art saliency detectors on six popular RGB-D SOD benchmarks.