Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval

TL;DR

This paper introduces a novel data augmentation framework using StyleGAN2 to generate paired text-image data, significantly enhancing fine-grained image-to-text retrieval performance.

Contribution

It proposes a new method for paired data augmentation by leveraging StyleGAN2 and latent space alignment to improve cross-modal retrieval tasks.

Findings

Augmented data improves retrieval accuracy.

The method outperforms baseline models.

Enhanced generalization on public datasets.

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.