IBNet: Interactive Branch Network for salient object detection

Abstract

At present, saliency detection methods have achieved gratifying progress benefiting from the development of deep learning. However, the existing methods always fail to make full use of the label information. To address this problem, we focus on the complementarity of salient body information and salient detail information within the labels and propose the Interactive Branch Network (IBNet) in this paper. Generally, IBNet contains three components of Label Redefinition Module (LRM), Information Exchange Module (IEM) and Connected Flow Loss (CFL). These three components all play an enormously important role in the complementary performance of detection. In LRM, enough useful and meaningful heuristic knowledge from the given labels is expanded for dynamic and collaborative supervised learning. In IEM, different derived branches are assigned to collect different types of features for interactive fusion. In CFL, the losses from all connected branches are merged to calculate the total loss. Extensive experiments on benchmark datasets exhibit the effectiveness and efficiency of the proposed method against the state-of-the-art approaches. The source code is publicly available athttps://github.com/xianfangfx/IBNet.