# cmSalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1912.10280 · HTML: https://mlsanity.com/paper/1912.10280/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2019.

## Built on by (most cited first, 21 of 21)

- [Uncertainty-aware Joint Salient Object and Camouflaged Object Detection](https://mlsanity.com/paper/2104.02628/) (2021)
- [Specificity-preserving RGB-D Saliency Detection](https://mlsanity.com/paper/2108.08162/) (2021)
- [SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection](https://mlsanity.com/paper/2204.05585/) (2022)
- [Does Thermal Really Always Matter for RGB-T Salient Object Detection?](https://mlsanity.com/paper/2210.04266/) (2022)
- [HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection](https://mlsanity.com/paper/2301.03036/) (2023)
- [SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition](https://mlsanity.com/paper/2308.04369/) (2023)
- [Spatio-Temporal Self-Attention Network for Video Saliency Prediction](https://mlsanity.com/paper/2108.10696/) (2021)
- [Towards Diverse Binary Segmentation via A Simple yet General Gated Network](https://mlsanity.com/paper/2303.10396/) (2023)
- [Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images](https://mlsanity.com/paper/2103.00809/) (2021)
- [Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks](https://mlsanity.com/paper/2411.18288/) (2024)
- [Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation](https://mlsanity.com/paper/2501.09947/) (2025)
- [KAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection](https://mlsanity.com/paper/2504.05878/) (2025)
- [Semi-supervised Salient Object Detection with Effective Confidence Estimation](https://mlsanity.com/paper/2112.14019/) (2021)
- [Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2206.03105/) (2022)
- [Perception-and-Regulation Network for Salient Object Detection](https://mlsanity.com/paper/2107.12560/) (2021)
- [Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2108.06281/) (2021)
- [ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2109.04627/) (2021)
- [HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2307.00954/) (2023)
- [Focus Entirety and Perceive Environment for Arbitrary-Shaped Text Detection](https://mlsanity.com/paper/2409.16827/) (2024)
- [Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object Detection](https://mlsanity.com/paper/2501.01648/) (2025)
- [Enhancing Semi-Supervised Multi-View Graph Convolutional Networks via Supervised Contrastive Learning and Self-Training](https://mlsanity.com/paper/2512.13770/) (2025)

## Abstract (arXiv)

Image salient object detection (SOD) is an active research topic in computer vision and multimedia area. Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem. The main challenge for RGB-D salient object detection is how to exploit the salient cues of both intra-modality (RGB, depth) and cross-modality simultaneously which is known as cross-modality detection problem. In this paper, we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network (cmSalGAN). cmSalGAN aims to learn an optimal view-invariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework, which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem. To further improve the detection results, the attention mechanism and edge detection module are also incorporated into cmSalGAN. The entire cmSalGAN can be trained in an end-to-end manner by using the standard deep neural network framework. Experimental results show that cmSalGAN achieves the new state-of-the-art RGB-D saliency detection performance on several benchmark datasets.