# Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection

arXiv: https://arxiv.org/abs/2108.06281 · HTML: https://mlsanity.com/paper/2108.06281/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [BAM: Bottleneck Attention Module](https://mlsanity.com/paper/1807.06514/) (2018)
- [Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks](https://mlsanity.com/paper/1907.06781/) (2019)
- [Pyramid Feature Attention Network for Saliency detection](https://mlsanity.com/paper/1903.00179/) (2019)
- [Suppress and Balance: A Simple Gated Network for Salient Object Detection](https://mlsanity.com/paper/2007.08074/) (2020)
- [DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2003.08608/) (2020)
- [Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion](https://mlsanity.com/paper/1710.05174/) (2017)
- [cmSalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks](https://mlsanity.com/paper/1912.10280/) (2019)
- [Boundary-guided Feature Aggregation Network for Salient Object Detection](https://mlsanity.com/paper/1809.10821/) (2018)

## Abstract (arXiv)

The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance effective multi-modal information in the feature fusion phase. In this letter, we propose a novel gated recoding network (GRNet) to evaluate the information validity of the two modes, and balance their influence. Our framework is divided into three phases: perception phase, recoding mixing phase and feature integration phase. First, A perception encoder is adopted to extract multi-level single-modal features, which lays the foundation for multi-modal semantic comparative analysis. Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder. The recoding mixer is responsible for recoding and mixing the balanced multi-modal information. Finally, the hybrid branch decoder completes the multi-level feature integration under the guidance of an optional edge guidance stream (OEGS). Experiments and analysis on eight popular benchmarks verify that our framework performs favorably against 9 state-of-art methods.