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Depth guided feature selection for RGBD salient object detection
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AI
DOI：10.1016/j.neucom.2022.11.030.png)
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Depth information can greatly benefit the saliency detection in RGBD images if they are utilized well. Prevalent methods generally directly fuse depth and RGB features in networks. However, due to the inherent inconsistent between RGB and depth information, the RGB features are easy to be interfered by the intrinsic noise existed in depth features, making the precise RGBD saliency detection still a chal-lenge. In this paper, we propose a novel Depth Guided Feature Selection network (DGFSnet) that takes depth information as prior and dynamically selects the complementary RGB information for RGBD salient object detection. Specifically, DGFSnet first includes a Depth Weight Generation module (DWG) to learn a set of layer-specific weights from multi-scale depth features. Guiding by these learned weights, DGFSnet further devises a Weight-guided Feature Aggregation module (WFA) to assign them to their correspond-ing RGB layers for dynamically enhancing and selecting saliency-related RGB features. With two modules, DGFSnet is able to effectively integrate the multi-modality complementaries and further highlight salient regions. Experimental results over seven popular RGBD salient object detection benchmarks demonstrate that DGFSnet fairly locates salient regions and effectively segments the complete object.(c) 2022 Elsevier B.V. All rights reserved.
Keyword:
RGBD salient object detection
Depth weight guider
Feature aggregation
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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Depth-Aware Salient Object Detection and Segmentation via Multiscale Discriminative Saliency Fusion and Bootstrap Learning基于多尺度判别显著性融合和Bootstrap学习的深度感知显著目标检测和分割
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