Progressive Guided Fusion Network with Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection
文献类型：期刊论文
| 作者 | J. Wu; G. Han; H. Wang; H. Yang; Q. Li; D. Liu; F. Ye and P. Liu |
| 刊名 | IEEE Access
 |
| 出版日期 | 2021 |
| 卷号 | 9页码:150608-150622 |
| ISSN号 | 21693536 |
| DOI | 10.1109/ACCESS.2021.3126338 |
| 英文摘要 | The depth map contains abundant spatial structure cues, which makes it extensively introduced into saliency detection tasks for improving the detection accuracy. Nevertheless, the acquired depth map is often with uneven quality, due to the interference of depth sensors and external environments, posing a challenge when trying to minimize the disturbances from low-quality depth maps during the fusion process. In this article, to mitigate such issues and highlight the salient objects, we propose a progressive guided fusion network (PGFNet) with multi-modal and multi-scale attention for RGB-D salient object detection. Particularly, we first present a multi-modal and multi-scale attention fusion model (MMAFM) to fully mine and utilize the complementarity of features at different scales and modalities for achieving optimal fusion. Then, to strengthen the semantic expressiveness of the shallow-layer features, we design a multi-modal feature refinement mechanism (MFRM), which exploits the high-level fusion feature to guide the enhancement of the shallow-layer original RGB and depth features before they are fused. Moreover, a residual prediction module (RPM) is applied to further suppress background elements. Our entire network adopts a top-down strategy to progressively excavate and integrate valuable information. Compared with the state-of-the-art methods, experimental results demonstrate the effectiveness of our proposed method both qualitatively and quantitatively on eight challenging benchmark datasets. 2013 IEEE. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/65557]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | J. Wu,G. Han,H. Wang,et al. Progressive Guided Fusion Network with Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection[J]. IEEE Access,2021,9:150608-150622. |
| APA | J. Wu.,G. Han.,H. Wang.,H. Yang.,Q. Li.,...&F. Ye and P. Liu.(2021).Progressive Guided Fusion Network with Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection.IEEE Access,9,150608-150622. |
| MLA | J. Wu,et al."Progressive Guided Fusion Network with Multi-Modal and Multi-Scale Attention for RGB-D Salient Object Detection".IEEE Access 9(2021):150608-150622. |
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