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Enhancing Intra-modal Similarity in a Cross-Modal Triplet Loss

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Discovery Science (DS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14276))

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Abstract

Cross-modal retrieval requires building a common latent space that captures and correlates information from different data modalities, usually images and texts. Cross-modal training based on the triplet loss with hard negative mining is a state-of-the-art technique to address this problem. This paper shows that such approach is not always effective in handling intra-modal similarities. Specifically, we found that this method can lead to inconsistent similarity orderings in the latent space, where intra-modal pairs with unknown ground-truth similarity are ranked higher than cross-modal pairs representing the same concept. To address this problem, we propose two novel loss functions that leverage intra-modal similarity constraints available in a training triplet but not used by the original formulation. Additionally, this paper explores the application of this framework to unsupervised image retrieval problems, where cross-modal training can provide the supervisory signals that are otherwise missing in the absence of category labels. Up to our knowledge, we are the first to evaluate cross-modal training for intra-modal retrieval without labels.

We present comprehensive experiments on MS-COCO and Flickr30K, demonstrating the advantages and limitations of the proposed methods in cross-modal and intra-modal retrieval tasks in terms of performance and novelty measures. Our code is publicly available on GitHub https://github.com/MariodotR/FullHN.git.

This research was partially funded by National Agency for Research and Development (ANID, Chile), grant numbers FONDEF IT21I0019, ANID PIA/APOYO AFB180002 and ANID-Basal Project FB0008.

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Notes

  1. 1.

    For MS-COCO, we report results for 5k images.

  2. 2.

    With results of \([ 351.3 - 350.3 , 351.0-349.3, 350.4-351.4]\), respectively.

  3. 3.

    With results of [367.0, 368.1, 375.9, 377.5, 370.4], respectively.

  4. 4.

    The best value is underlined and the best without considering TERAN is highlighted in bold.

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Authors and Affiliations

  1. Universidad Federico Santa María, Valparaíso, Chile

    Mario Mallea, Ricardo Nanculef & Mauricio Araya

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  1. Mario Mallea
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  2. Ricardo Nanculef
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  3. Mauricio Araya
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Corresponding author

Correspondence to Mario Mallea .

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Editors and Affiliations

  1. Waikato University, Hamilton, New Zealand

    Albert Bifet

  2. Aeronautics Institute of Technology, São José dos Campos, Brazil

    Ana Carolina Lorena

  3. University of Porto, Porto, Portugal

    Rita P. Ribeiro

  4. University of Porto, Porto, Portugal

    João Gama

  5. University of Coimbra, Coimbra, Portugal

    Pedro H. Abreu

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Mallea, M., Nanculef, R., Araya, M. (2023). Enhancing Intra-modal Similarity in a Cross-Modal Triplet Loss. In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham. https://doi.org/10.1007/978-3-031-45275-8_17

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  • DOI: https://doi.org/10.1007/978-3-031-45275-8_17

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