# RGBD Salient Object Detection via Deep Fusion

arXiv: https://arxiv.org/abs/1607.03333 · HTML: https://mlsanity.com/paper/1607.03333/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2016.

## Built on by (most cited first, 30 of 46)

- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction](https://mlsanity.com/paper/2112.13528/) (2021)
- [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)
- [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)
- [SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2207.07898/) (2022)
- [Fantastic Animals and Where to Find Them: Segment Any Marine Animal with Dual SAM](https://mlsanity.com/paper/2404.04996/) (2024)
- [Alignment-Free RGBT Salient Object Detection: Semantics-guided Asymmetric Correlation Network and A Unified Benchmark](https://mlsanity.com/paper/2406.00917/) (2024)
- [Learning Adaptive Fusion Bank for Multi-modal Salient Object Detection](https://mlsanity.com/paper/2406.01127/) (2024)
- [Generative Transformer for Accurate and Reliable Salient Object Detection](https://mlsanity.com/paper/2104.10127/) (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)
- [CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation](https://mlsanity.com/paper/2307.08098/) (2023)
- [Salient Object Detection in RGB-D Videos](https://mlsanity.com/paper/2310.15482/) (2023)
- [GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2203.10785/) (2022)
- [Learning Synergistic Attention for Light Field Salient Object Detection](https://mlsanity.com/paper/2104.13916/) (2021)
- [Adapting Segment Anything Model to Multi-modal Salient Object Detection with Semantic Feature Fusion Guidance](https://mlsanity.com/paper/2408.15063/) (2024)
- [Middle-level Fusion for Lightweight RGB-D Salient Object Detection](https://mlsanity.com/paper/2104.11543/) (2021)
- [MutualFormer: Multi-Modality Representation Learning via Cross-Diffusion Attention](https://mlsanity.com/paper/2112.01177/) (2021)
- [Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection](https://mlsanity.com/paper/2204.13456/) (2022)
- [Pyramidal Attention for Saliency Detection](https://mlsanity.com/paper/2204.06788/) (2022)
- [Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth](https://mlsanity.com/paper/2303.10937/) (2023)
- [An Energy-Based Prior for Generative Saliency](https://mlsanity.com/paper/2204.08803/) (2022)
- [Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2206.03105/) (2022)
- [M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2109.07922/) (2021)
- [Transformer-based Network for RGB-D Saliency Detection](https://mlsanity.com/paper/2112.00582/) (2021)
- [Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images](https://mlsanity.com/paper/2201.00100/) (2022)
- [Densely Deformable Efficient Salient Object Detection Network](https://mlsanity.com/paper/2102.06407/) (2021)

## Abstract (arXiv)

Numerous efforts have been made to design different low level saliency cues for the RGBD saliency detection, such as color or depth contrast features, background and color compactness priors. However, how these saliency cues interact with each other and how to incorporate these low level saliency cues effectively to generate a master saliency map remain a challenging problem. In this paper, we design a new convolutional neural network (CNN) to fuse different low level saliency cues into hierarchical features for automatically detecting salient objects in RGBD images. In contrast to the existing works that directly feed raw image pixels to the CNN, the proposed method takes advantage of the knowledge in traditional saliency detection by adopting various meaningful and well-designed saliency feature vectors as input. This can guide the training of CNN towards detecting salient object more effectively due to the reduced learning ambiguity. We then integrate a Laplacian propagation framework with the learned CNN to extract a spatially consistent saliency map by exploiting the intrinsic structure of the input image. Extensive quantitative and qualitative experimental evaluations on three datasets demonstrate that the proposed method consistently outperforms state-of-the-art methods.