FANet: Feature aggregation network for RGBD saliency detection

• Published in: Signal processing. Image communication Vol. 102; p. 116591
• Main Authors: Zhou, Xiaofei, Wen, Hongfa, Shi, Ran, Yin, Haibing, Zhang, Jiyong, Yan, Chenggang
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-03-2022; Elsevier BV
• Subjects: Agglomeration; Color imagery; Feature aggregation; Feature extraction; Graph neural networks; Hierarchical fusion; Modules; RGBD saliency; Salience; RGBD saliency; Graph neural networks; Feature aggregation; Hierarchical fusion
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/j.image.2021.116591

The crucial issue in RGBD saliency detection is how to adequately mine and fuse the geometric information and the appearance information contained in depth maps and RGB images, respectively. In this paper, we propose a novel feature aggregation network FANet including a feature extraction module and an aggregation module for RGBD saliency detection. The premier characteristic of FANet is the feature aggregation module consisting of a designed region enhanced module (REM) and a series of deployed hierarchical fusion module (HFM). Specifically, on one hand, the REM provides the powerful capability in differentiating salient objects and background. On the other hand, the HFM is used to gradually integrate high-level semantic information and low-level spatial details, where the K-nearest neighbor graph neural networks (KGNNs) and the non-local module (NLM) are embedded into HFM to dig the geometric information and enhance high-level appearance features, respectively. Extensive experiments on five RGBD datasets show that our model achieves compelling performance against the current 11 state-of-the-art RGBD saliency models.
•We propose a novel feature aggregation network (FANet) for RGBD saliency detection.•Region enhanced module REM is used to differentiate salient regions and backgrounds.•Hierarchical fusion module HFM is used to aggregate multi-modal cues.•HFM is supported by the graph neural networks (KGNNs) and the non-local module (NLM).•Extensive experimental results verify the effectiveness of the proposed FANet.