Work

Adaptive Fusion for RGB-D Salient Object Detection

Year: 2019

Type: article

Abstract: RGB-D (red, green, blue, and depth) salient object detection aims to identify the most visually distinctive objects in a pair of color and depth images. Based upon an observation that most of the salient objects may stand out at least in one modality, this paper proposes an adaptive fusion scheme to fuse saliency predictions generated from two modalities. Specifically, we design two-streamed convolutional neural networks (CNN), each of which extracts features and predicts a saliency map from either RGB or depth modality. Then, a saliency fusion module learns a switch map that is used to adaptively fuse the predicted saliency maps. A loss function composed of saliency supervision, switch map supervision, and edge-preserving constraints are designed to make full supervision, and the entire network is trained in an end-to-end manner. Benefited from the adaptive fusion strategy and the edge-preserving constraint, our approach outperforms state-of-the-art methods on three publicly available datasets.

Source: IEEE Access

DOI: 10.1109/access.2019.2913107

Authors: Ningning Wang, Xiaojin Gong

Institution: Zhejiang University

Language: English

Topic: Visual Attention and Saliency Detection

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,750

Funder: Natural Science Foundation of Zhejiang Province