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| Adversarial Cross-Modal Retrieval Based on Association Constraint |
| GUO Qian1,3, QIAN Yuhua1,2,3, LIANG Xinyan1,3 |
1. Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006
2. Key Laboratory Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006
3. School of Computer and Information Technology, Shanxi University, Taiyuan 030006 |
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Abstract In the existing cross-modal retrieval methods, retrieval results are obtained via the subspace acquired by a certain index constraint such as distance or similarity. Since the subspaces are learned with different index constraints, retrieval results are different. To improve the robustness of common subspace, a method for adversarial cross-modal retrieval based on association constraint is proposed. The consistency of different modality features is improved by the adversarial constraint to make the discriminator in the constraint unable to distinguish which modality the subspace features come from. The association of different modality features is enhanced by the association constraint. The structural information between example pairs with the same semantics of different modalities and different semantics of the same modality is taken into account by the triple loss constraint. Experimental results on datasets show that the proposed method is more effective than other cross-modal retrieval methods.
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Received: 15 October 2020
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| Fund:National Natural Science Foundation of China(No.61672332,61802238,61603228,62006146,61906115,F060308), Key Research and Development Program of Shanxi Province(International Science and Technology Cooperation Project) (No.201903D421003), Program for Outstanding Innovative Teams of Higher Learning Institutions of Shanxi Province, Program for San Jin Young Scholars of Shanxi Province, Overseas Returnee Research Program of Shanxi Province(No.2017023,2018172,HGKY2019001), Shanxi Province Science Foundation for Youths(No.201901D211171, 201901D211169), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(No.2020L0036) |
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Corresponding Authors:
QIAN Yuhua, Ph.D., professor. His research interests include pattern recognition, feature selection, rough set theory, granular computing and artificial intelligence.
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About author:: GUO Qian, Ph.D. candidate. Her research interests include deep learning, cross-modal retrieval and logic learning.
LIANG Xinyan, Ph.D. candidate. His research interests include multimodal data fusion and cross-modal retrieval. |
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2021, Vol. 34 
