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 viewinvariant 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.

中文翻译：

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cmSalGAN：使用交叉视图生成对抗网络进行 RGB-D 显着对象检测

图像显着目标检测（SOD）是计算机视觉和多媒体领域的一个活跃的研究课题。融合 RGB 和深度的互补信息已被证明对于图像显着对象检测是有效的，这被称为 RGB-D 显着对象检测问题。RGB-D 显着对象检测的主要挑战是如何同时利用模态内（RGB、深度）和跨模态的显着线索，这被称为跨模态检测问题。在本文中，我们通过设计一种新颖的跨模态显着性生成对抗网络 (cmSalGAN) 来应对这一挑战。cmSalGAN 旨在通过新颖的对抗性学习框架为 RGB 和深度图像学习最佳的视图不变和一致的像素级表示，因此，它同时结合了内部视图信息和交叉视图图像的相关信息，用于 RGB-D 显着性检测问题。为了进一步提高检测结果，cmSalGAN 中还加入了注意力机制和边缘检测模块。可以使用标准的深度神经网络框架以端到端的方式训练整个 cmSalGAN。实验结果表明，cmSalGAN 在几个基准数据集上实现了新的最先进的 RGB-D 显着性检测性能。可以使用标准的深度神经网络框架以端到端的方式训练整个 cmSalGAN。实验结果表明，cmSalGAN 在几个基准数据集上实现了新的最先进的 RGB-D 显着性检测性能。可以使用标准的深度神经网络框架以端到端的方式训练整个 cmSalGAN。实验结果表明，cmSalGAN 在几个基准数据集上实现了新的最先进的 RGB-D 显着性检测性能。