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 will be publicly available at https://github.com/xianfangfx/IBNet after the paper is accepted.

目前，受益于深度学习的发展，显着性检测方法取得了可喜的进展。然而，现有的方法总是不能充分利用标签信息。为了解决这个问题，我们关注标签内显着体信息和显着细节信息的互补性，并在本文中提出了交互式分支网络（IBNet）。一般来说，IBNet包含标签重定义模块（LRM）、信息交换模块（IEM）和连通流损失（CFL）三个组件。这三个组件都在检测的互补性能中发挥着极其重要的作用。在 LRM 中，来自给定标签的足够有用和有意义的启发式知识被扩展用于动态和协作监督学习。在 IEM 中，分配不同的派生分支以收集不同类型的特征以进行交互融合。在 CFL 中，合并所有连接分支的损失以计算总损失。对基准数据集的大量实验证明了所提出的方法相对于最先进方法的有效性和效率。论文录用后，源代码将在https://github.com/xianfangfx/IBNet公开。