首页|Discriminative unimodal feature selection and fusion for RGB-D salient object detection
Discriminative unimodal feature selection and fusion for RGB-D salient object detection
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NSTL
Elsevier
Most existing RGB-D salient object detectors make use of the complementary information of RGB-D im-ages to overcome the challenging scenarios, e.g., low contrast, clutter backgrounds. However, these mod-els generally neglect the fact that one of the input images may be poor in quality. This will adversely affect the discriminative ability of cross-modal features when the two channels are fused directly. To ad-dress this issue, a novel end-to-end RGB-D salient object detection model is proposed in this paper. At the core of our model is a Semantic-Guided Modality-Weight Map Generation (SG-MWMG) sub-network, producing modality-weight maps to indicate which regions on both modalities are high-quality regions, given input RGB-D images and the guidance of their semantic information. Based on it, a Bi-directional Multi-scale Cross-modal Feature Fusion (Bi-MCFF) module is presented, where the interactions of the features across different modalities and scales are exploited by using a novel bi-directional structure for better capturing cross-scale and cross-modal complementary information. The experimental results on several benchmark datasets verify the effectiveness and superiority of the proposed method over some state-of-the-art methods. (c) 2021 Published by Elsevier Ltd.
NSTL
Elsevier
