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Title: Paired cross-modal data augmentation for fine-grained image-to-text retrieval
Authors: Wang, Hao
Lin, Guosheng
Hoi, Steven C. H.
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Wang, H., Lin, G., Hoi, S. C. H. & Miao, C. (2022). Paired cross-modal data augmentation for fine-grained image-to-text retrieval. 30th ACM International Conference on Multimedia (MM 2022), 5517-5526.
Project: AISG-GC-2019-003
Abstract: This paper investigates an open research problem of generating text-image pairs to improve the training of fine-grained image-to-text cross-modal retrieval task, and proposes a novel framework for paired data augmentation by uncovering the hidden semantic information of StyleGAN2 model. Specifically, we first train a StyleGAN2 model on the given dataset. We then project the real images back to the latent space of StyleGAN2 to obtain the latent codes. To make the generated images manipulatable, we further introduce a latent space alignment module to learn the alignment between StyleGAN2 latent codes and the corresponding textual caption features. When we do online paired data augmentation, we first generate augmented text through random token replacement, then pass the augmented text into the latent space alignment module to output the latent codes, which are finally fed to StyleGAN2 to generate the augmented images. We evaluate the efficacy of our augmented data approach on two public cross-modal retrieval datasets, in which the promising experimental results demonstrate the augmented text-image pair data can be trained together with the original data to boost the image-to-text cross-modal retrieval performance.
ISBN: 9781450392037
DOI: 10.1145/3503161.3547809
Rights: © 2022 The owner/author(s). Publication rights licensed to ACM. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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