Feature Sharing Cooperative Network for Semantic Segmentation-猫眼课题宝

DOI：

10.5220/0010312505770584

发表时间：

2021-01

期刊：

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作者：

Ryota Ikedo;K. Hotta

近年来，深度神经网络在图像识别领域取得了很高的精度。受人类学习方法的启发，我们提出了一种基于合作学习的语义分割方法，这种方法类似于群体学习，可以共享信息。我们使用两个相同的网络和路径在两个网络之间发送特征映射。同时训练两个网络。通过共享特征映射，两个网络中的一个可以获得单个网络无法获得的信息。此外，为了增强协作程度，我们提出了仅连接同一层和多层的两种方法。我们在两种网络上评估了我们提出的想法。一种是双注意力网络（DANet），另一种是DeepLabv3+。与传统的单一网络和网络集成相比，该方法具有更好的分割精度。

In recent years, deep neural networks have achieved high accuracy in the field of image recognition. By inspired from human learning method, we propose a semantic segmentation method using cooperative learning which shares the information resembling a group learning. We use two same networks and paths for sending feature maps between two networks. Two networks are trained simultaneously. By sharing feature maps, one of two networks can obtain the information that cannot be obtained by a single network. In addition, in order to enhance the degree of cooperation, we propose two kinds of methods that connect only the same layer and multiple layers. We evaluated our proposed idea on two kinds of networks. One is Dual Attention Network (DANet) and the other one is DeepLabv3+. The proposed method achieved better segmentation accuracy than the conventional single network and ensemble of networks.

Feature Sharing Cooperative Network for Semantic Segmentation

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