Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion

• URL: http://arxiv.org/abs/2103.11832v1
• Date: Mon, 22 Mar 2021 13:28:45 GMT
• Title: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion
• Authors: Peng Sun, Wenhu Zhang, Huanyu Wang, Songyuan Li, Xi Li
• Abstract summary: RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. We propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. Experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.
• Score: 15.033234579900657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGBD feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling scheme using the depth-wise geometric prior of salient objects. In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior. Moreover, to perform effective multi-modal feature fusion, we further present an automatic architecture search approach for RGB-D SOD, which does well in finding out a feasible architecture from our specially designed multi-modal multi-scale search space. Extensive experiments on seven standard benchmarks demonstrate the effectiveness of the proposed approach against the state-of-the-art.

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