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Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning

文献类型：期刊论文

作者Yao, Hantao2; Min, Shaobo1; Zhang, Yongdong1; Xu, Changsheng2,3
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2022
卷号24页码:1933-1942
关键词Semantics Visualization Bridges Training Knowledge transfer Image recognition Topology Transductive Zero-Shot Learning Graph Attribute Embedding Attribute-Induced Bias Eliminating Semantic-Visual Alignment
ISSN号1520-9210
DOI10.1109/TMM.2021.3074252
通讯作者Yao, Hantao(hantao.yao@nlpr.ia.ac.cn)
英文摘要Transductive zero-shot learning is designed to recognize unseen categories by aligning both visual and semantic information in a joint embedding space. Four types of domain biases exist in Transductive ZSL, i.e., visual bias and semantic bias in two domains, and two visual-semantic biases exist in the seen and unseen domains. However, the existing work has only focused on specific components of these topics, leading to severe semantic ambiguity during knowledge transfer. To solve this problem, we propose a novel attribute-induced bias eliminating (AIBE) module for Transductive ZSL. Specifically, for the visual bias between the two domains, the mean-teacher module is first used to bridge the visual representation discrepancy between the two domains using unsupervised learning and unlabeled images. Then, an attentional graph attribute embedding process is proposed to reduce the semantic bias between seen and unseen categories using a graph operation to describe the semantic relationship between categories. To reduce semantic-visual bias in the seen domain, we align the visual center of each category with the corresponding semantic attributes instead of with the individual visual data point, which preserves the semantic relationship in the embedding space. Finally, for the semantic-visual bias in the unseen domain, an unseen semantic alignment constraint is designed to align visual and semantic space using an unsupervised process. The evaluations on several benchmarks demonstrate the effectiveness of the proposed method, e.g., 82.8%/75.5%, 97.1%/82.5%, and 73.2%/52.1% for Conventional/Generalized ZSL settings for CUB, AwA2, and SUN datasets, respectively.
资助项目National Key Research and Development Program of China[2018AAA0102200] ; National Natural Science Foundation of China[61902399] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[62002355] ; Beijing Natural Science Foundation[L201001] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000778959200012
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Key Research Program of Frontier Sciences, CAS
源URL[http://ir.ia.ac.cn/handle/173211/48335]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Yao, Hantao
作者单位1.Univ Sci & Technol China, Natl Engn Lab Brain Inspired Intelligence Technol, Hefei 230026, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yao, Hantao,Min, Shaobo,Zhang, Yongdong,et al. Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2022,24:1933-1942.
APA Yao, Hantao,Min, Shaobo,Zhang, Yongdong,&Xu, Changsheng.(2022).Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning.IEEE TRANSACTIONS ON MULTIMEDIA,24,1933-1942.
MLA Yao, Hantao,et al."Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning".IEEE TRANSACTIONS ON MULTIMEDIA 24(2022):1933-1942.

入库方式： OAI收割

来源：自动化研究所

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