Survey of Zero-Shot Image Classification
It is time-consuming and laborious to manually label a large number of samples, and samples from some rare classes are difficult to obtain. Therefore, the zero-shot image classification has become a research hotspot in the computer vision field. Firstly, the zero-shot learning, including direct push...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2021-05-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2683.shtml |
| _version_ | 1819144107767889920 |
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| author | LIU Jingyi, SHI Caijuan, TU Dongjing, LIU Shuai |
| author_facet | LIU Jingyi, SHI Caijuan, TU Dongjing, LIU Shuai |
| author_sort | LIU Jingyi, SHI Caijuan, TU Dongjing, LIU Shuai |
| collection | DOAJ |
| description | It is time-consuming and laborious to manually label a large number of samples, and samples from some rare classes are difficult to obtain. Therefore, the zero-shot image classification has become a research hotspot in the computer vision field. Firstly, the zero-shot learning, including direct push zero-shot learning and inductive zero-shot learning, is introduced briefly. Secondly, the space embedding zero-shot image classification methods and the generative model based zero-shot image classification methods with their subclass methods are introduced emphatically. Meanwhile, the mechanism, advantages and disadvantages, and application scenarios of these methods are analyzed and summarized. Thirdly, the main datasets and main evaluation criteria for zero-shot image classification are briefly introduced, and the performance of typical zero-shot image classification methods is compared. Then, the problems such as domain drift, hubness and semantic gap and the corresponding solutions are pointed out. Finally, the future development trends and research hotspots of zero-shot image classification are discussed, such as the accurate location of discriminative region, visual features of high-quality unseen class, generalized zero-shot image classification, etc. |
| first_indexed | 2024-12-22T12:36:53Z |
| format | Article |
| id | doaj.art-f4de8a8de9934b92bf9a629e23ed8581 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-22T12:36:53Z |
| publishDate | 2021-05-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-f4de8a8de9934b92bf9a629e23ed85812022-12-21T18:25:34ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182021-05-0115581282410.3778/j.issn.1673-9418.2010092Survey of Zero-Shot Image ClassificationLIU Jingyi, SHI Caijuan, TU Dongjing, LIU Shuai01. College of Artificial Intelligence, North China University of Science and Technology, Tangshan, Hebei 063210, China 2. College of Electronics and Information Engineering, Shenzhen University, Shenzhen, Guangdong 518060, ChinaIt is time-consuming and laborious to manually label a large number of samples, and samples from some rare classes are difficult to obtain. Therefore, the zero-shot image classification has become a research hotspot in the computer vision field. Firstly, the zero-shot learning, including direct push zero-shot learning and inductive zero-shot learning, is introduced briefly. Secondly, the space embedding zero-shot image classification methods and the generative model based zero-shot image classification methods with their subclass methods are introduced emphatically. Meanwhile, the mechanism, advantages and disadvantages, and application scenarios of these methods are analyzed and summarized. Thirdly, the main datasets and main evaluation criteria for zero-shot image classification are briefly introduced, and the performance of typical zero-shot image classification methods is compared. Then, the problems such as domain drift, hubness and semantic gap and the corresponding solutions are pointed out. Finally, the future development trends and research hotspots of zero-shot image classification are discussed, such as the accurate location of discriminative region, visual features of high-quality unseen class, generalized zero-shot image classification, etc.http://fcst.ceaj.org/CN/abstract/abstract2683.shtmlzero-shot learningzero-shot image classificationembedding spacegenerative modeldeep learning |
| spellingShingle | LIU Jingyi, SHI Caijuan, TU Dongjing, LIU Shuai Survey of Zero-Shot Image Classification Jisuanji kexue yu tansuo zero-shot learning zero-shot image classification embedding space generative model deep learning |
| title | Survey of Zero-Shot Image Classification |
| title_full | Survey of Zero-Shot Image Classification |
| title_fullStr | Survey of Zero-Shot Image Classification |
| title_full_unstemmed | Survey of Zero-Shot Image Classification |
| title_short | Survey of Zero-Shot Image Classification |
| title_sort | survey of zero shot image classification |
| topic | zero-shot learning zero-shot image classification embedding space generative model deep learning |
| url | http://fcst.ceaj.org/CN/abstract/abstract2683.shtml |
| work_keys_str_mv | AT liujingyishicaijuantudongjingliushuai surveyofzeroshotimageclassification |