Towards Diverse Binary Segmentation via A Simple yet General Gated Network

• URL: http://arxiv.org/abs/2303.10396v2
• Date: Fri, 3 May 2024 11:41:30 GMT
• Title: Towards Diverse Binary Segmentation via A Simple yet General Gated Network
• Authors: Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, Lei Zhang,
• Abstract summary: We propose a simple yet general gated network (GateNet) to tackle binary segmentation tasks. With the help of multi-level gate units, the valuable context information from the encoder can be selectively transmitted to the decoder. We introduce a "Fold" operation to improve the atrous convolution and form a novel folded atrous convolution.
• Score: 71.19503376629083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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