# Modality-Guided Subnetwork for Salient Object Detection

arXiv: https://arxiv.org/abs/2110.04904 · HTML: https://mlsanity.com/paper/2110.04904/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Enhanced-alignment Measure for Binary Foreground Map Evaluation](https://mlsanity.com/paper/1805.10421/) (2018)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [Receptive Field Block Net for Accurate and Fast Object Detection](https://mlsanity.com/paper/1711.07767/) (2017)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection](https://mlsanity.com/paper/2004.08515/) (2020)
- [Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.06227/) (2020)
- [A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection](https://mlsanity.com/paper/2007.06811/) (2020)
- [Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion](https://mlsanity.com/paper/2103.11832/) (2021)
- [Deep Contrast Learning for Salient Object Detection](https://mlsanity.com/paper/1603.01976/) (2016)
- [Accurate RGB-D Salient Object Detection via Collaborative Learning](https://mlsanity.com/paper/2007.11782/) (2020)
- [RGB-D Salient Object Detection with Cross-Modality Modulation and Selection](https://mlsanity.com/paper/2007.07051/) (2020)
- [Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation](https://mlsanity.com/paper/2004.04534/) (2020)
- [Learning Uncertain Convolutional Features for Accurate Saliency Detection](https://mlsanity.com/paper/1708.02031/) (2017)
- [Is Depth Really Necessary for Salient Object Detection?](https://mlsanity.com/paper/2006.00269/) (2020)
- [PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection](https://mlsanity.com/paper/1803.08636/) (2018)
- [Depth-Adapted CNN for RGB-D cameras](https://mlsanity.com/paper/2009.09976/) (2020)

## Built on by (most cited first, 8 of 8)

- [Source-free Depth for Object Pop-out](https://mlsanity.com/paper/2212.05370/) (2022)
- [RGB-Event Fusion for Moving Object Detection in Autonomous Driving](https://mlsanity.com/paper/2209.08323/) (2022)
- [HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness](https://mlsanity.com/paper/2301.07405/) (2023)
- [HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection](https://mlsanity.com/paper/2301.03036/) (2023)
- [Robust RGB-D Fusion for Saliency Detection](https://mlsanity.com/paper/2208.01762/) (2022)
- [Object Segmentation by Mining Cross-Modal Semantics](https://mlsanity.com/paper/2305.10469/) (2023)
- [PDDM: Pseudo Depth Diffusion Model for RGB-PD Semantic Segmentation Based in Complex Indoor Scenes](https://mlsanity.com/paper/2503.18393/) (2025)
- [Mamba-based Spatio-Frequency Motion Perception for Video Camouflaged Object Detection](https://mlsanity.com/paper/2507.23601/) (2025)

## Abstract (arXiv)

Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the identification of salient objects with complicated scenarios. However, most RGBD networks require multi-modalities from the input side and feed them separately through a two-stream design, which inevitably results in extra costs on depth sensors and computation. To tackle these inconveniences, we present in this paper a novel fusion design named modality-guided subnetwork (MGSnet). It has the following superior designs: 1) Our model works for both RGB and RGBD data, and dynamically estimating depth if not available. Taking the inner workings of depth-prediction networks into account, we propose to estimate the pseudo-geometry maps from RGB input - essentially mimicking the multi-modality input. 2) Our MGSnet for RGB SOD results in real-time inference but achieves state-of-the-art performance compared to other RGB models. 3) The flexible and lightweight design of MGS facilitates the integration into RGBD two-streaming models. The introduced fusion design enables a cross-modality interaction to enable further progress but with a minimal cost.