In RGBD saliency detection research field, RGB and depth cues are generally given the same status by RGBD saliency models. However, they ignore that both modalities are significantly different in inherent attribution so that effective features cannot be drawn from depth maps. In order to address this issue, this paper proposes a novel RGBD saliency model including two key components: the contrast-guided depth feature extraction (CDFE) module and the cross-modal feature integration (CFI) module. Specifically, considering the specific properties of depth information, we first design a targeted CDFE module, which learns multi-level deep depth features by strengthening the depth contrast between foreground and background, to provide multi-level deep depth features. Then, to sufficiently and reasonably integrate multi-level cross-modal features, namely the multi-level deep RGB and depth features, we equip the saliency inference branch with the CFI module, which contains two successive steps, i.e. information enrichment and feature enhancement. Extensive experiments are conducted on five challenging RGBD datasets, and the experimental results clearly demonstrate the effectiveness and superiority of the proposed model against the state-of-the-art RGBD saliency models.

在RGBD显着性研究领域，RGBD显着性模型通常赋予RGB和深度提示相同的状态。但是，他们忽略了两种模式在固有属性上都存在显着差异，因此无法从深度图中得出有效特征。为了解决这个问题，本文提出了一种新颖的RGBD显着性模型，该模型包括两个关键组件：对比度引导的深度特征提取（CDFE）模块和交叉模式特征集成（CFI）模块。具体来说，考虑到深度信息的特定属性，我们首先设计一个目标CDFE模块，该模块通过增强前景和背景之间的深度对比来学习多级深度深度特征，以提供多级深度深度特征。然后，为了充分合理地整合多级跨模式功能，即信息丰富和功能增强。在五个具有挑战性的RGBD数据集上进行了广泛的实验，实验结果清楚地证明了该模型相对于最新的RGBD显着性模型的有效性和优越性。