RGB-D Image Saliency Detection Based on Multi-modal Feature-fused Supervision

Abstract

RGB-D saliency detection identifies the most visually attentive target areas in a pair of RGB and Depth images. Existing two-stream networks, which treat RGB and Depth data equally, are almost identical in feature extraction. As the lower layers Depth features with a lot of noise, it causes image features not be well characterized. Therefore, a multi-modal feature-fused supervision of RGB-D saliency detection network is proposed, RGB and Depth data are studied independently through two-stream , double-side supervision module is used respectively to obtain saliency maps of each layer, and then the multi-modal feature-fused module is used to later three layers of the fused RGB and Depth of higher dimensional information to generate saliency predicted results. Finally, the information of lower layers is fused to generate the ultimate saliency maps. Experiments on three open data sets show that the proposed network has better performance and stronger robustness than the current RGB-D saliency detection models.