<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-long-2023-style">
    <titleInfo>
        <title>Style-Aware Contrastive Learning for Multi-Style Image Captioning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yucheng</namePart>
        <namePart type="family">Zhou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Guodong</namePart>
        <namePart type="family">Long</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EACL 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dubrovnik, Croatia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style. However, existing methods overlook the relationship between linguistic style and visual content. To overcome this drawback, we propose style-aware contrastive learning for multi-style image captioning. First, we present a style-aware visual encoder with contrastive learning to mine potential visual content relevant to style. Moreover, we propose a style-aware triplet contrast objective to distinguish whether the image, style and caption matched. To provide positive and negative samples for contrastive learning, we present three retrieval schemes: object-based retrieval, RoI-based retrieval and triplet-based retrieval, and design a dynamic trade-off function to calculate retrieval scores. Experimental results demonstrate that our approach achieves state-of-the-art performance. In addition, we conduct an extensive analysis to verify the effectiveness of our method.</abstract>
    <identifier type="citekey">zhou-long-2023-style</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-eacl.169</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-eacl.169/</url>
    </location>
    <part>
        <date>2023-05</date>
        <extent unit="page">
            <start>2257</start>
            <end>2267</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Style-Aware Contrastive Learning for Multi-Style Image Captioning
%A Zhou, Yucheng
%A Long, Guodong
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F zhou-long-2023-style
%X Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style. However, existing methods overlook the relationship between linguistic style and visual content. To overcome this drawback, we propose style-aware contrastive learning for multi-style image captioning. First, we present a style-aware visual encoder with contrastive learning to mine potential visual content relevant to style. Moreover, we propose a style-aware triplet contrast objective to distinguish whether the image, style and caption matched. To provide positive and negative samples for contrastive learning, we present three retrieval schemes: object-based retrieval, RoI-based retrieval and triplet-based retrieval, and design a dynamic trade-off function to calculate retrieval scores. Experimental results demonstrate that our approach achieves state-of-the-art performance. In addition, we conduct an extensive analysis to verify the effectiveness of our method.
%R 10.18653/v1/2023.findings-eacl.169
%U https://aclanthology.org/2023.findings-eacl.169/
%U https://doi.org/10.18653/v1/2023.findings-eacl.169
%P 2257-2267

Markdown (Informal)

[Style-Aware Contrastive Learning for Multi-Style Image Captioning](https://aclanthology.org/2023.findings-eacl.169/) (Zhou & Long, Findings 2023)

• Style-Aware Contrastive Learning for Multi-Style Image Captioning (Zhou & Long, Findings 2023)

ACL

• Yucheng Zhou and Guodong Long. 2023. Style-Aware Contrastive Learning for Multi-Style Image Captioning. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2257–2267, Dubrovnik, Croatia. Association for Computational Linguistics.