# Bifurcated backbone strategy for RGB-D salient object detection

arXiv: https://arxiv.org/abs/2007.02713 · HTML: https://mlsanity.com/paper/2007.02713/

## Key facts

- Cited by 15 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 15 of 15)

- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation](https://mlsanity.com/paper/2309.09668/) (2023)
- [CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection](https://mlsanity.com/paper/2112.02363/) (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)
- [HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness](https://mlsanity.com/paper/2301.07405/) (2023)
- [ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection](https://mlsanity.com/paper/2406.12536/) (2024)
- [Towards Accurate RGB-D Saliency Detection with Complementary Attention and Adaptive Integration](https://mlsanity.com/paper/2102.04046/) (2021)
- [Densely Deformable Efficient Salient Object Detection Network](https://mlsanity.com/paper/2102.06407/) (2021)
- [A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network](https://mlsanity.com/paper/2401.11914/) (2024)
- [FusionSAM: Visual Multi-Modal Learning with Segment Anything](https://mlsanity.com/paper/2408.13980/) (2024)
- [Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2201.09574/) (2022)
- [Dynamic Message Propagation Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2206.09552/) (2022)
- [A bioinspired three-stage model for camouflaged object detection](https://mlsanity.com/paper/2305.12635/) (2023)
- [Graph-Based Uncertainty Modeling and Multimodal Fusion for Salient Object Detection](https://mlsanity.com/paper/2508.20415/) (2025)

## Abstract (arXiv)

Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to devise a novel cascaded refinement network. In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS). Second, we introduce a depth-enhanced module (DEM) to excavate informative depth cues from the channel and spatial views. Then, RGB and depth modalities are fused in a complementary way. Our architecture, named Bifurcated Backbone Strategy Network (BBS-Net), is simple, efficient, and backbone-independent. Extensive experiments show that BBS-Net significantly outperforms eighteen SOTA models on eight challenging datasets under five evaluation measures, demonstrating the superiority of our approach (~ 4 % improvement in S-measure vs. the top-ranked model: DMRA-iccv2019). In addition, we provide a comprehensive analysis on the generalization ability of different RGB-D datasets and provide a powerful training set for future research.