Boosting deep cross-modal retrieval hashing with adversarially robust training

doi:10.1007/s10489-023-04715-0

	Boosting deep cross-modal retrieval hashing with adversarially robust training
	Zhang, Xingwei 1,2; Zheng, Xiaolong1,2 ; Mao, Wenji1,2 ; Zeng, Daniel Dajun1,2
发表期刊	APPLIED INTELLIGENCE
ISSN	0924-669X
	2023-07-13
页码	13
通讯作者	Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
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.
关键词	Cross-modal retrieval Adversarial training Deep hashing model Deep neural network
DOI	10.1007/s10489-023-04715-0
收录类别	SCI
语种	英语
资助项目	Ministry of Science and Technology of China[:2020AAA0108401] ; Natural Science Foundation of China[72225011] ; Natural Science Foundation of China[71621002]
项目资助者	Ministry of Science and Technology of China ; Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001027468800010
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53760
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.Univ Chinese Acad Sci, Sch Artificial Intelligence, 19 Yuquan Rd, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100053, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.