In many binary segmentation tasks, most CNNs-based methods use a U-shape encoder-decoder network as their basic structure. They ignore two key problems when the encoder exchanges information with the decoder: one is the lack of interference control mechanism between them, the other is without considering the disparity of the contributions from different encoder levels. In this work, we propose a simple yet general gated network (GateNet) to tackle them all at once. With the help of multi-level gate units, the valuable context information from the encoder can be selectively transmitted to the decoder. In addition, we design a gated dual branch structure to build the cooperation among the features of different levels and improve the discrimination ability of the network. Furthermore, we introduce a “Fold” operation to improve the atrous convolution and form a novel folded atrous convolution, which can be flexibly embedded in ASPP or DenseASPP to accurately localize foreground objects of various scales. GateNet can be easily generalized to many binary segmentation tasks, including general and specific object segmentation and multi-modal segmentation. Without bells and whistles, our network consistently performs favorably against the state-of-the-art methods under 10 metrics on 33 datasets of 10 binary segmentation tasks.

在许多二值分割任务中，大多数基于 CNN 的方法都使用 U 形编码器-解码器网络作为其基本结构。他们忽略了编码器与解码器交换信息时的两个关键问题：一是它们之间缺乏干扰控制机制，二是没有考虑不同编码器级别的贡献差异。在这项工作中，我们提出了一个简单而通用的门控网络（GateNet）来同时解决所有这些问题。在多级门单元的帮助下，来自编码器的有价值的上下文信息可以选择性地传输到解码器。此外，我们设计了门控双分支结构，以建立不同级别特征之间的协作，提高网络的判别能力。此外，我们引入了“Fold”操作来改进空洞卷积，形成一种新颖的折叠空洞卷积，它可以灵活地嵌入ASPP或DenseASPP中，以准确定位各种尺度的前景物体。 GateNet 可以很容易地推广到许多二元分割任务，包括通用和特定对象分割以及多模态分割。没有花里胡哨的东西，我们的网络在 10 个二进制分割任务的 33 个数据集上的 10 个指标下始终表现优于最先进的方法。