@article{Chen2024, 
author = {Bojian Chen and Wenbin Wu and Zhezhou Li and Tengfei Han and Zhuolei Chen and Weihao Zhang},
title = {Attention-guided cross-modal multiple feature aggregation network for RGB-D salient object detection},
year = {2024},
journal = {Electronic Research Archive},
volume = {32},
number = {1},
pages = {643-669},
keywords = {salient object detection (SOD), RGB-D, feature aggregation, attention, cross-modal},
url = {https://www.sciopen.com/article/10.3934/era.2024031},
doi = {10.3934/era.2024031},
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.}
}