1. Attention-guided cross-modal multiple feature aggregation network for RGB-D salient object detection
- Author
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Bojian Chen, Wenbin Wu, Zhezhou Li, Tengfei Han, Zhuolei Chen, and Weihao Zhang
- Subjects
salient object detection (sod) ,rgb-d ,feature aggregation ,attention ,cross-modal ,Mathematics ,QA1-939 ,Applied mathematics. Quantitative methods ,T57-57.97 - Abstract
The goal of RGB-D salient object detection is to aggregate the information of the two modalities of RGB and depth to accurately detect and segment salient objects. Existing RGB-D SOD models can extract the multilevel features of single modality well and can also integrate cross-modal features, but it can rarely handle both at the same time. To tap into and make the most of the correlations of intra- and inter-modality information, in this paper, we proposed an attention-guided cross-modal multi-feature aggregation network for RGB-D SOD. Our motivation was that both cross-modal feature fusion and multilevel feature fusion are crucial for RGB-D SOD task. The main innovation of this work lies in two points: One is the cross-modal pyramid feature interaction (CPFI) module that integrates multilevel features from both RGB and depth modalities in a bottom-up manner, and the other is cross-modal feature decoder (CMFD) that aggregates the fused features to generate the final saliency map. Extensive experiments on six benchmark datasets showed that the proposed attention-guided cross-modal multiple feature aggregation network (ACFPA-Net) achieved competitive performance over 15 state of the art (SOTA) RGB-D SOD methods, both qualitatively and quantitatively.
- Published
- 2024
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