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Parsing human image by fusing semantic and spatial features: A deep learning approach
Information Processing & Management ( IF 8.6 ) Pub Date : 2020-05-28 , DOI: 10.1016/j.ipm.2020.102306
Ruilin Zhao , Yanbing Xue , Jing Cai , Zan Gao

How to parse the human image to obtain the text label corresponding to the human body is a critical task for human-computer interaction. Although previous methods have significantly improved the parsing performance, the problem of parsing confusion and tiny target missing remains unresolved, which leads to errors and incomplete inference accordingly. Targeting at these drawbacks, we fuse semantic and spatial features to mine the human body information based on the Dual Pyramid Unit convolutional neural network, named as DPUNet. DPUNet is composed of Context Pyramid Unit (CPU) and Spatial Pyramid Unit (SPU). Firstly, we design the CPU to aggregate the local to global semantic information, which exports the semantic feature for eliminating the semantic confusion. To capture the tiny targets for preventing the details from missing, the SPU is proposed to incorporate the multi-scale spatial information and output the spatial feature. Finally, the features of two complementary units are fused for accurate and complete human parsing results. Our approach achieves more excellent performance than the state-of-the-art methods on single human and multiple human parsing datasets. Meanwhile, the proposed framework is efficient with a fast speed of 41.2fps.



中文翻译:

通过融合语义和空间特征来解析人的图像:一种深度学习方法

如何解析人的图像以获得与人体相对应的文本标签是人机交互的关键任务。尽管以前的方法已经大大提高了解析性能,但是解析混乱和目标丢失很小的问题仍然没有解决,这会导致错误和推断不完整。针对这些缺点,我们融合了语义和空间特征,以基于双重金字塔单元卷积神经网络DPUNet的方式挖掘人体信息。DPUNet由上下文金字塔单元(CPU)和空间金字塔单元(SPU)组成。首先,我们设计CPU来聚合本地信息到全局语义信息,从而导出语义特征以消除语义混乱。为了捕获微小的目标以防止细节丢失,提出将SPU纳入多尺度空间信息并输出空间特征。最后,将两个互补单元的功能融合在一起,以获得准确而完整的人工解析结果。与单人和多人解析数据集上的最新方法相比,我们的方法具有更高的性能。同时,所提出的框架是有效的,具有41.2fps的快速速度。

更新日期:2020-05-28
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