Perception-and-Regulation Network for Salient Object Detection

Abstract

Effective fusion of different types of features is the key to salient object detection. The majority of existing network structure design is based on the subjective experience of scholars and the process of feature fusion does not consider the relationship between the fused features and highest-level features. In this paper, we focus on the feature relationship and propose a novel global attention unit, which we term the "perception- and-regulation" (PR) block, that adaptively regulates the feature fusion process by explicitly modeling interdependencies between features. The perception part uses the structure of fully-connected layers in classification networks to learn the size and shape of objects. The regulation part selectively strengthens and weakens the features to be fused. An imitating eye observation module (IEO) is further employed for improving the global perception ability of the network. The imitation of foveal vision and peripheral vision enables IEO to scrutinize highly detailed objects and to organize the broad spatial scene to better segment objects. Sufficient experiments conducted on SOD datasets demonstrate that the proposed method performs favorably against 22 state-of-the-art methods.

References in corpus (9)

• ParseNet: Looking Wider to See Better
• Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
• Deeply supervised salient object detection with short connections
• Video Salient Object Detection via Fully Convolutional Networks
• DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution
• Re-thinking Co-Salient Object Detection
• CAGNet: Content-Aware Guidance for Salient Object Detection
• F3Net: Fusion, Feedback and Focus for Salient Object Detection
• Few-Cost Salient Object Detection with Adversarial-Paced Learning