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Dual discriminant adversarial cross-modal retrieval

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Abstract

In order to improve the accuracy of cross-modal retrieval tasks and achieve flexible retrieval between different modalities, we propose a Dual Discriminant Adversarial cross-modal Retrieval (DDAC) method in this paper. First, DDAC integrates adversarial learning and minimization of feature projection distances and introduces label information in it. It can eliminate the same semantic heterogeneity between modalities while maintaining the distinguishability of different semantic features between modalities. Then, cosine distance is used to minimize and maximize the inter-modal distance of features with the same and different labels respectively to solve the inter-modal discrimination problem. Different from the general method, DDAC performs dual discrimination in the label space and solves the intra-modal discrimination problem from two perspectives of probability distribution and distance. Extensive experiments carried out on three public datasets validate that the proposed DDAC outperforms the state-of-the-art methods.

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Authors and Affiliations

  1. College of Science, Guangxi University of Science and Technology, Liuzhou, 545000, China

    Pei He & Zhuo Wang

  2. Tus College of Digit, Guangxi University of Science and Technology, Liuzhou, 545000, China

    Meng Wang & Ding Tu

Authors
  1. Pei He
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  2. Meng Wang
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  3. Ding Tu
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  4. Zhuo Wang
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Correspondence to Meng Wang.

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He, P., Wang, M., Tu, D. et al. Dual discriminant adversarial cross-modal retrieval. Appl Intell 53, 4257–4267 (2023). https://doi.org/10.1007/s10489-022-03653-7

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