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Boosting deep cross-modal retrieval hashing with adversarially robust training

文献类型：期刊论文

作者Zhang, Xingwei1,2; Zheng, Xiaolong1,2; Mao, Wenji1,2; Zeng, Daniel Dajun1,2
刊名APPLIED INTELLIGENCE
出版日期2023-07-13
页码13
ISSN号0924-669X
关键词Cross-modal retrieval Adversarial training Deep hashing model Deep neural network
DOI10.1007/s10489-023-04715-0
通讯作者Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
英文摘要Deep hashing methods effectively enhance the performance of conventional machine learning retrieval models, particularly in visual medium evolving cross-modal retrieval tasks, by relying on the outstanding feature extraction ability of deep neural networks (DNNs). The state-of-the-art deep hashing research focuses on designing prominent models by employing DNNs to discover semantic information from different modalities of data and execute relevant information retrieval tasks. However, the robustness attribute considered essential for reliable DNN model design has limited concerns on deep hashing models. In this article, we present an end-to-end adversarial training framework for cross-modal retrieval. Our framework leverages a projected gradient descent(PGD)-based method to generate adversarial samples, which are then combined with normal samples to achieve robust training. Our approach addresses the vulnerability issues of existing cross-modal retrieval models and fills the gap in retrieval task design. We conduct extensive experiments and compare our model with state-of-the-art cross-modal retrieval models on three benchmark datasets to verify that our model can effectively boost the performance of deep hashing retrieval models on cross-modal retrieval . This work highlights the effectiveness of adversarial training in efficient deep hashing model design.
资助项目Ministry of Science and Technology of China[:2020AAA0108401] ; Natural Science Foundation of China[72225011] ; Natural Science Foundation of China[71621002]
WOS研究方向Computer Science
语种英语
出版者SPRINGER
WOS记录号WOS:001027468800010
资助机构Ministry of Science and Technology of China ; Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/53759]  
专题舆论大数据科学与技术应用联合实验室
通讯作者Zheng, Xiaolong
作者单位1.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100053, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 19 Yuquan Rd, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Xingwei,Zheng, Xiaolong,Mao, Wenji,et al. Boosting deep cross-modal retrieval hashing with adversarially robust training[J]. APPLIED INTELLIGENCE,2023:13.
APA Zhang, Xingwei,Zheng, Xiaolong,Mao, Wenji,&Zeng, Daniel Dajun.(2023).Boosting deep cross-modal retrieval hashing with adversarially robust training.APPLIED INTELLIGENCE,13.
MLA Zhang, Xingwei,et al."Boosting deep cross-modal retrieval hashing with adversarially robust training".APPLIED INTELLIGENCE (2023):13.

入库方式： OAI收割

来源：自动化研究所

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