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        <title>Style-Aware Contrastive Learning for Multi-Style Image Captioning</title>
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        <namePart type="given">Yucheng</namePart>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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    <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>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.169</identifier>
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%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.