This article proposes an innovative RGBD saliency model, that is, attention-guided feature integration network, which can extract and fuse features and perform saliency inference. Specifically, the model first extracts multimodal and level deep features. Then, a series of attention modules are deployed to the multilevel RGB and depth features, yielding enhanced deep features. Next, the enhanced multimodal deep features are hierarchically fused. Lastly, the RGB and depth boundary features, that is, low-level spatial details, are added to the integrated feature to perform saliency inference. The key points of the AFI-Net are the attention-guided feature enhancement and the boundary-aware saliency inference, where the attention module indicates salient objects coarsely, and the boundary information is used to equip the deep feature with more spatial details. Therefore, salient objects are well characterized, that is, well highlighted. The comprehensive experiments on five challenging public RGBD datasets clearly exhibit the superiority and effectiveness of the proposed AFI-Net.

中文翻译：

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AFI-Net：用于 RGBD 显着性检测的注意力引导特征集成网络

本文提出了一种创新的RGBD显着性模型，即注意力引导的特征集成网络，可以提取和融合特征并进行显着性推断。具体来说，该模型首先提取多模态和层次深度特征。然后，将一系列注意力模块部署到多级 RGB 和深度特征，从而产生增强的深度特征。接下来，对增强的多模态深度特征进行分层融合。最后，将RGB和深度边界特征（即低级空间细节）添加到集成特征中以执行显着性推断。AFI-Net的关键点是注意力引导的特征增强和边界感知显着性推断，其中注意力模块粗略地指示显着性对象，而边界信息用于为深层特征配备更多的空间细节。因此，显着对象得到很好的表征，即得到很好的突出显示。对五个具有挑战性的公共 RGBD 数据集的综合实验清楚地展示了所提出的 AFI-Net 的优越性和有效性。