Knowledge Commons of Institute of Automation,CAS
An Iterative Co-Training Transductive Framework for Zero Shot Learning | |
Liu, Bo1,2; Hu, Lihua3; Dong, Qiulei1,2,4; Hu, Zhanyi1,2 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2021 | |
卷号 | 30页码:6943-6956 |
摘要 | In zero-shot learning (ZSL) community, it is generally recognized that transductive learning performs better than inductive one as the unseen-class samples are also used in its training stage. How to generate pseudo labels for unseen-class samples and how to use such usually noisy pseudo labels are two critical issues in transductive learning. In this work, we introduce an iterative co-training framework which contains two different base ZSL models and an exchanging module. At each iteration, the two different ZSL models are co-trained to separately predict pseudo labels for the unseen-class samples, and the exchanging module exchanges the predicted pseudo labels, then the exchanged pseudo-labeled samples are added into the training sets for the next iteration. By such, our framework can gradually boost the ZSL performance by fully exploiting the potential complementarity of the two models' classification capabilities. In addition, our co-training framework is also applied to the generalized ZSL (GZSL), in which a semantic-guided OOD detector is proposed to pick out the most likely unseen-class samples before class-level classification to alleviate the bias problem in GZSL. Extensive experiments on three benchmarks show that our proposed methods could significantly outperform about 31 state-of-the-art ones. |
关键词 | Visualization Semantics Training Feature extraction Testing Detectors Predictive models Zero-shot learning transductive learning co-training |
DOI | 10.1109/TIP.2021.3100552 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China (NSFC)[61991423] ; National Natural Science Foundation of China (NSFC)[U1805264] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32050100] |
项目资助者 | National Natural Science Foundation of China (NSFC) ; Strategic Priority Research Program of the Chinese Academy of Sciences |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000682121800005 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45623 |
专题 | 多模态人工智能系统全国重点实验室_机器人视觉 |
通讯作者 | Dong, Qiulei |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100049, Peoples R China 3.Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan 030024, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Liu, Bo,Hu, Lihua,Dong, Qiulei,et al. An Iterative Co-Training Transductive Framework for Zero Shot Learning[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:6943-6956. |
APA | Liu, Bo,Hu, Lihua,Dong, Qiulei,&Hu, Zhanyi.(2021).An Iterative Co-Training Transductive Framework for Zero Shot Learning.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,6943-6956. |
MLA | Liu, Bo,et al."An Iterative Co-Training Transductive Framework for Zero Shot Learning".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):6943-6956. |
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