| Towards Human-Machine Recognition Alignment: An Adversarilly Robust Multimodal Retrieval Hashing Framework | |
Zhang, Xingwei1,2; Zheng, Xiaolong1,2 ; Liu, Bin3; Wang, Xiao1,2; Mao, Wenji1,2; Zeng, Daniel Dajun1,2; Wang, Fei-Yue1,2
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| Source Publication | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
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| ISSN | 2329-924X |
| 2022-08-29 | |
| Pages | 13 |
| Corresponding Author | Zheng, Xiaolong(xiaolong.zheng@ia.ac.cn) |
| Abstract | The multimodality nature of web data has necessitated complex multimodal information retrieval for a wide range of web applications. Deep neural networks (DNNs) have been widely employed to extract semantic features from raw samples to improve retrieval accuracy. In addition, hashing is widely used to improve computational and storage efficiency. As such, deep hashing frameworks have been applied for multimodal retrieval tasks. However, there is still a great recognitive gap between primate brain structure-inspired DNNs and humans. On computer vision tasks, well-crafted DNN models can be easily defeated by invisible small attacks, and this phenomenon indicates a large recognition gap between DNN models and humans. Recently, adversarial defense methods have been shown to improve the human-machine recognition alignment in several classification tasks. However, the robustness problem on the retrieval tasks, especially on the deep hashing-based multimodal retrieval models, is still not well studied. Therefore, in this article, we present an adversarially robust training mechanism to improve model robustness for the purpose of human-machine recognition alignment on retrieval tasks. Through extensive experimental results on several social multimodal retrieval benchmarks, we show that the robust training hashing framework proposed can mitigate the recognition gap on retrieval tasks. Our study highlights the necessity of robustness enhancement on deep hashing models. |
| Keyword | Training Task analysis Semantics Perturbation methods Feature extraction Computational modeling Robustness Adversarial perturbation adversarially robust training deep hashing multimodal retrieval |
| DOI | 10.1109/TCSS.2022.3199819 |
| WOS Keyword | NEURAL-NETWORKS ; MODELS |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Ministry of Science and Technology of China[2020AAA0108401] ; Natural Science Foundation of China[72225011] ; Natural Science Foundation of China[71621002] |
| Funding Organization | Ministry of Science and Technology of China ; Natural Science Foundation of China |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Cybernetics ; Computer Science, Information Systems |
| WOS ID | WOS:000849234000001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50057 |
| Collection | 复杂系统管理与控制国家重点实验室_互联网大数据与信息安全 复杂系统管理与控制国家重点实验室_平行智能技术与系统团队 |
| Corresponding Author | Zheng, Xiaolong |
| Affiliation | 1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 3.West Virginia Univ, Dept Management Informat Syst, Morgantown, WV 26506 USA |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Zhang, Xingwei,Zheng, Xiaolong,Liu, Bin,et al. Towards Human-Machine Recognition Alignment: An Adversarilly Robust Multimodal Retrieval Hashing Framework[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2022:13. |
| APA | Zhang, Xingwei.,Zheng, Xiaolong.,Liu, Bin.,Wang, Xiao.,Mao, Wenji.,...&Wang, Fei-Yue.(2022).Towards Human-Machine Recognition Alignment: An Adversarilly Robust Multimodal Retrieval Hashing Framework.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,13. |
| MLA | Zhang, Xingwei,et al."Towards Human-Machine Recognition Alignment: An Adversarilly Robust Multimodal Retrieval Hashing Framework".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2022):13. |
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