Generalized Zero Shot Learning via Synthesis Pseudo Features
Compared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes. The most existing GZSL methods learn a visual-semantic mapping function to bridge the knowledge transfer from seen to unseen classes by us...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8746253/ |
| _version_ | 1818412627072647168 |
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| author | Chuanlong Li Xiufen Ye Haibo Yang Yatong Han Xiang Li Yunpeng Jia |
| author_facet | Chuanlong Li Xiufen Ye Haibo Yang Yatong Han Xiang Li Yunpeng Jia |
| author_sort | Chuanlong Li |
| collection | DOAJ |
| description | Compared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes. The most existing GZSL methods learn a visual-semantic mapping function to bridge the knowledge transfer from seen to unseen classes by using semantic information and other labeled training data. However, these methods often suffer from severe performance degradation because they ignore similar structures between different classes. To solve these problems, we propose a GZSL method that transforms GZSL problems to conventional supervised learning ones by synthesizing pseudo features for unseen classes. This technique has two key aspects. The first one is the synthesis strategy; the proposed strategy directly synthesizes the pseudo features of unseen classes contrary to current synthesis-based methods, which synthesize pseudo instances. Our method regards the combination of N features of instances as the pseudo features. These N features belong to N different classes that are similar to unseen ones. This synthesis strategy is in line with the cognitive style of human beings. The second key aspect is that we preserve the similar structures between seen and unseen classes. Inspired by the center loss method, we assign each semantic vector as the center of deep features in the training stage. This way preserves the similar structures between the classes. Such preservation can be beneficial for improving classification accuracy. The experimental results on four benchmark datasets demonstrate that our model outperforms state-of-the-art methods for the GZSL. The source code is available at https://github.com/guizilaile23/SPF-GZSL. |
| first_indexed | 2024-12-14T10:50:19Z |
| format | Article |
| id | doaj.art-62a67f5501df429fa9d7148f095d9cd5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:50:19Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-62a67f5501df429fa9d7148f095d9cd52022-12-21T23:05:15ZengIEEEIEEE Access2169-35362019-01-017878278783610.1109/ACCESS.2019.29250938746253Generalized Zero Shot Learning via Synthesis Pseudo FeaturesChuanlong Li0https://orcid.org/0000-0002-4297-3447Xiufen Ye1Haibo Yang2Yatong Han3Xiang Li4Yunpeng Jia5College of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCollege of Automation, Harbin Engineering University, Harbin, ChinaCompared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes. The most existing GZSL methods learn a visual-semantic mapping function to bridge the knowledge transfer from seen to unseen classes by using semantic information and other labeled training data. However, these methods often suffer from severe performance degradation because they ignore similar structures between different classes. To solve these problems, we propose a GZSL method that transforms GZSL problems to conventional supervised learning ones by synthesizing pseudo features for unseen classes. This technique has two key aspects. The first one is the synthesis strategy; the proposed strategy directly synthesizes the pseudo features of unseen classes contrary to current synthesis-based methods, which synthesize pseudo instances. Our method regards the combination of N features of instances as the pseudo features. These N features belong to N different classes that are similar to unseen ones. This synthesis strategy is in line with the cognitive style of human beings. The second key aspect is that we preserve the similar structures between seen and unseen classes. Inspired by the center loss method, we assign each semantic vector as the center of deep features in the training stage. This way preserves the similar structures between the classes. Such preservation can be beneficial for improving classification accuracy. The experimental results on four benchmark datasets demonstrate that our model outperforms state-of-the-art methods for the GZSL. The source code is available at https://github.com/guizilaile23/SPF-GZSL.https://ieeexplore.ieee.org/document/8746253/Generalized zero-shot learningpseudo feature synthesisimage classificationsupervised learningmulti-class classification |
| spellingShingle | Chuanlong Li Xiufen Ye Haibo Yang Yatong Han Xiang Li Yunpeng Jia Generalized Zero Shot Learning via Synthesis Pseudo Features IEEE Access Generalized zero-shot learning pseudo feature synthesis image classification supervised learning multi-class classification |
| title | Generalized Zero Shot Learning via Synthesis Pseudo Features |
| title_full | Generalized Zero Shot Learning via Synthesis Pseudo Features |
| title_fullStr | Generalized Zero Shot Learning via Synthesis Pseudo Features |
| title_full_unstemmed | Generalized Zero Shot Learning via Synthesis Pseudo Features |
| title_short | Generalized Zero Shot Learning via Synthesis Pseudo Features |
| title_sort | generalized zero shot learning via synthesis pseudo features |
| topic | Generalized zero-shot learning pseudo feature synthesis image classification supervised learning multi-class classification |
| url | https://ieeexplore.ieee.org/document/8746253/ |
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