A Universal Decoupled Training Framework for Human Parsing
Human parsing is an important technology in human–robot interaction systems. At present, the distribution of multi-category human parsing datasets is unbalanced, and the samples present a long-tailed distribution, which directly affects the performance of human parsing. Meanwhile, the similarity bet...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/16/5964 |
| _version_ | 1827617560712970240 |
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| author | Yang Li Huahong Zuo Ping Han |
| author_facet | Yang Li Huahong Zuo Ping Han |
| author_sort | Yang Li |
| collection | DOAJ |
| description | Human parsing is an important technology in human–robot interaction systems. At present, the distribution of multi-category human parsing datasets is unbalanced, and the samples present a long-tailed distribution, which directly affects the performance of human parsing. Meanwhile, the similarity between different categories leads the model to predict false parsing results. To solve the above problems, a general decoupled training framework called Decoupled Training framework based on Pixel Resampling (DTPR) was proposed to solve the long-tailed distribution, and a new sampling method named Pixel Resampling based on Accuracy distribution (PRA) for semantic segmentation was also proposed and applied to this decoupled training framework. The framework divides the training process into two phases, the first phase is to improve the model feature extraction ability, and the second phase is to improve the performance of the model on tail categories. The training framework was evaluated in MHPv2.0 and LIP datasets, and tested in both high-precision and real-time SOTA models. The MPA metric of model trained by DTPR in above two datasets increased by more than 6%, and the mIoU metric increased by more than 1% without changing the model structure. |
| first_indexed | 2024-03-09T09:49:50Z |
| format | Article |
| id | doaj.art-404f342fac8a416b9fed16ee83f6d423 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T09:49:50Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-404f342fac8a416b9fed16ee83f6d4232023-12-02T00:16:17ZengMDPI AGSensors1424-82202022-08-012216596410.3390/s22165964A Universal Decoupled Training Framework for Human ParsingYang Li0Huahong Zuo1Ping Han2School of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaWuhan Chuyan Information Technology Co., Ltd., Wuhan 430030, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan 430070, ChinaHuman parsing is an important technology in human–robot interaction systems. At present, the distribution of multi-category human parsing datasets is unbalanced, and the samples present a long-tailed distribution, which directly affects the performance of human parsing. Meanwhile, the similarity between different categories leads the model to predict false parsing results. To solve the above problems, a general decoupled training framework called Decoupled Training framework based on Pixel Resampling (DTPR) was proposed to solve the long-tailed distribution, and a new sampling method named Pixel Resampling based on Accuracy distribution (PRA) for semantic segmentation was also proposed and applied to this decoupled training framework. The framework divides the training process into two phases, the first phase is to improve the model feature extraction ability, and the second phase is to improve the performance of the model on tail categories. The training framework was evaluated in MHPv2.0 and LIP datasets, and tested in both high-precision and real-time SOTA models. The MPA metric of model trained by DTPR in above two datasets increased by more than 6%, and the mIoU metric increased by more than 1% without changing the model structure.https://www.mdpi.com/1424-8220/22/16/5964pixel resamplinglong-tailed distributionhuman parsingsemantic segmentation |
| spellingShingle | Yang Li Huahong Zuo Ping Han A Universal Decoupled Training Framework for Human Parsing Sensors pixel resampling long-tailed distribution human parsing semantic segmentation |
| title | A Universal Decoupled Training Framework for Human Parsing |
| title_full | A Universal Decoupled Training Framework for Human Parsing |
| title_fullStr | A Universal Decoupled Training Framework for Human Parsing |
| title_full_unstemmed | A Universal Decoupled Training Framework for Human Parsing |
| title_short | A Universal Decoupled Training Framework for Human Parsing |
| title_sort | universal decoupled training framework for human parsing |
| topic | pixel resampling long-tailed distribution human parsing semantic segmentation |
| url | https://www.mdpi.com/1424-8220/22/16/5964 |
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