An adversarial cross-modal retrieval method based on collaborative attention networks

JiaKang Deng; YongGuo Han; Jing Liao

doi:10.1117/12.2673294

14 April 2023 An adversarial cross-modal retrieval method based on collaborative attention networks

JiaKang Deng, YongGuo Han, Jing Liao

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130K (2023) https://doi.org/10.1117/12.2673294
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Serious feature heterogeneity and semantic gaps exist between multi-source heterogeneous data, and existing cross-modal retrieval methods cannot effectively extract common semantic and complementary information between multi-source heterogeneous data. In this regard, an adversarial cross-modal retrieval method that fuses collaborative attention networks is proposed. The method addresses two major challenges in the cross-modal retrieval process, firstly, an information extraction algorithm based on the cooperative attention mechanism is designed, and secondly, the cooperative attention network is combined with the adversarial subspace learning algorithm to enhance the complementary capability of information in the feature subspace. The experimental results show that the proposed method has better retrieval results than similar cross-modal retrieval methods in terms of MAP metrics.

Citation Download Citation

JiaKang Deng, YongGuo Han, and Jing Liao "An adversarial cross-modal retrieval method based on collaborative attention networks", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130K (14 April 2023); https://doi.org/10.1117/12.2673294

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KEYWORDS

Data modeling

Semantics

Education and training

Feature extraction

Visualization

Performance modeling

Associative arrays

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