Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164145
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. https://dx.doi.org/10.1145/3503161.3547809
Project: AISG-GC-2019-003 
NRF-NRFI05-2019-0002 
MOH/NIC/HAIG03/2017 
AISG-RP-2018-003 
RG95/20 
Conference: 30th ACM International Conference on Multimedia (MM 2022)
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.
URI: https://hdl.handle.net/10356/164145
ISBN: 9781450392037
DOI: 10.1145/3503161.3547809
Schools: School of Computer Science and Engineering 
Rights: © 2022 The owner/author(s). Publication rights licensed to ACM. All rights reserved. This paper was published in the Proceedings of 30th ACM International Conference on Multimedia (MM 2022) and is made available with permission of The owner/author(s).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

Files in This Item:
File Description SizeFormat 
Full text.pdf1.51 MBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations 50

2
Updated on Feb 12, 2024

Page view(s)

172
Updated on Feb 24, 2024

Download(s) 50

44
Updated on Feb 24, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.