Dual guidance enhanced network for light field salient object detection

Abstract

Saliency detection models using light field data as input have not been thoroughly explored. Existing deep saliency models usually treat multi-focus images as independent information and extract their features separately, which may be cumbersome and over-rely on well-designed network structure. Besides, they do not fully explore the cross-modal complementarity and cross-level continuity of information, and rarely consider edge cues. Based on the above observations, in this paper, we investigate a novel Dual Guidance Enhanced Network (DGENet), which considers both spatial content and explicit boundary cues. Specifically, DGENet contains two key modules, i.e., the recurrent global-guided focus module (RGFM) and the boundary-guided semantic accumulation module (BSAM). These two modules are composed of multiple units, and the units in each module are not independent of each other. RGFM is used to distill out effective squeezed information of focal slices and RGB images between different levels. The learned global context features guide the network to focus on the salient region via a progressive reverse attention-driven strategy. Furthermore, BSAM introduces salient edge features to guide the accumulation of salient object features to generate salient maps with sharp boundaries. Extensive experiments on three challenging light field datasets demonstrate that our DGENet is superior to cutting-edge 2D, 3D and 4D methods.