# A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network

arXiv: https://arxiv.org/abs/2401.11914 · HTML: https://mlsanity.com/paper/2401.11914/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2007.01947/) (2020)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks](https://mlsanity.com/paper/2303.03667/) (2023)
- [End-to-end Learning of Deep Visual Representations for Image Retrieval](https://mlsanity.com/paper/1610.07940/) (2016)
- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [Source-free Depth for Object Pop-out](https://mlsanity.com/paper/2212.05370/) (2022)
- [CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection](https://mlsanity.com/paper/2210.02843/) (2022)
- [RGB-D Salient Object Detection via 3D Convolutional Neural Networks](https://mlsanity.com/paper/2101.10241/) (2021)
- [TRACER: Extreme Attention Guided Salient Object Tracing Network](https://mlsanity.com/paper/2112.07380/) (2021)
- [MSSNet: Multi-Scale-Stage Network for Single Image Deblurring](https://mlsanity.com/paper/2202.09652/) (2022)
- [HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness](https://mlsanity.com/paper/2301.07405/) (2023)
- [MobileSal: Extremely Efficient RGB-D Salient Object Detection](https://mlsanity.com/paper/2012.13095/) (2020)
- [Self-Supervised Pretraining for RGB-D Salient Object Detection](https://mlsanity.com/paper/2101.12482/) (2021)
- [Bifurcated backbone strategy for RGB-D salient object detection](https://mlsanity.com/paper/2007.02713/) (2020)
- [BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection](https://mlsanity.com/paper/2104.01784/) (2021)

## Built on by (most cited first, 1 of 1)

- [SPLF-SAM: Self-Prompting Segment Anything Model for Light Field Salient Object Detection](https://mlsanity.com/paper/2508.19746/) (2025)

## Abstract (arXiv)

Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. However, fusing features of different scales alwaysresults in large model sizes, impeding the application of multiscale CNNs in RGB-D saliency detection. In this paper, we propose a customized feature fusion module, called Saliency Enhanced Feature Fusion (SEFF), for RGB-D saliency detection. SEFF utilizes saliency maps of the neighboring scales to enhance the necessary features for fusing, resulting in more representative fused features. Our multiscale RGB-D saliency detector uses SEFF and processes images with three different scales. SEFF is used to fuse the features of RGB and depth images, as well as the features of decoders at different scales. Extensive experiments on five benchmark datasets have demonstrated the superiority of our method over ten SOTA saliency detectors.