A Novel Human Parsing Method Driven by Multi-Scale Feature Blend Network
Pages 30 - 38
Abstract
In recent years, human parsing has been developed a lot for its valuable utilization. However, existing methods have not fully solved semantic errors and incomplete semantic predictions. In this regard, a Multi-Scale Feature Blend Network(MFBNet) is proposed to deal with these problems from the respective of fusing multi-scale features. Specifically, we creatively introduce the Context Embedding module which uses the feature pyramid as the main structure to blend multi-scale feature information. Besides, ResNet-101 is applied as the backbone network to train and optimize shared weights and map the generated feature maps to the Context Embedding module. Experimental results on several wide-used datasets show that the proposed method outperforms than the state-of-art methods in human parsing.
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- A Novel Human Parsing Method Driven by Multi-Scale Feature Blend Network
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Published In
April 2021
82 pages
ISBN:9781450384940
DOI:10.1145/3467691
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 September 2021
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- Research-article
- Research
- Refereed limited
Funding Sources
- Anhui Natural Science Foundation
- Provincial Key Research and Development Program of Anhui
- Youth Fund of National Natural Science Foundation of China
Conference
ICRSA 2021
ICRSA 2021: 2021 4th International Conference on Robot Systems and Applications
April 9 - 11, 2021
Chengdu, China
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