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        <title>Semantically-Prompted Language Models Improve Visual Descriptions</title>
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        <namePart type="given">Michael</namePart>
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        <name type="personal">
            <namePart type="given">Helena</namePart>
            <namePart type="family">Gomez</namePart>
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    <abstract>Language-vision models like CLIP have made significant strides in vision tasks, such as zero-shot image classification (ZSIC). However, generating specific and expressive visual descriptions remains challenging; descriptions produced by current methods are often ambiguous and lacking in granularity. To tackle these issues, we propose V-GLOSS: Visual Glosses, a novel method built upon two key ideas. The first is Semantic Prompting, which conditions a language model on structured semantic knowledge. The second is a new contrastive algorithm that elicits fine-grained distinctions between similar concepts. With both ideas, we demonstrate that V-GLOSS improves visual descriptions and achieves strong results in the zero-shot setting on general and fine-grained image-classification datasets, including ImageNet, STL-10, FGVC Aircraft, and Flowers 102. Moreover, these descriptive capabilities contribute to enhancing image-generation performance. Finally, we introduce a quality-tested silver dataset with descriptions generated with V-GLOSS for all ImageNet classes.</abstract>
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%0 Conference Proceedings
%T Semantically-Prompted Language Models Improve Visual Descriptions
%A Ogezi, Michael
%A Hauer, Bradley
%A Kondrak, Grzegorz
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F ogezi-etal-2024-semantically
%X Language-vision models like CLIP have made significant strides in vision tasks, such as zero-shot image classification (ZSIC). However, generating specific and expressive visual descriptions remains challenging; descriptions produced by current methods are often ambiguous and lacking in granularity. To tackle these issues, we propose V-GLOSS: Visual Glosses, a novel method built upon two key ideas. The first is Semantic Prompting, which conditions a language model on structured semantic knowledge. The second is a new contrastive algorithm that elicits fine-grained distinctions between similar concepts. With both ideas, we demonstrate that V-GLOSS improves visual descriptions and achieves strong results in the zero-shot setting on general and fine-grained image-classification datasets, including ImageNet, STL-10, FGVC Aircraft, and Flowers 102. Moreover, these descriptive capabilities contribute to enhancing image-generation performance. Finally, we introduce a quality-tested silver dataset with descriptions generated with V-GLOSS for all ImageNet classes.
%U https://aclanthology.org/2024.findings-naacl.267
%P 4285-4302

Markdown (Informal)

[Semantically-Prompted Language Models Improve Visual Descriptions](https://aclanthology.org/2024.findings-naacl.267) (Ogezi et al., Findings 2024)

• Semantically-Prompted Language Models Improve Visual Descriptions (Ogezi et al., Findings 2024)

ACL

• Michael Ogezi, Bradley Hauer, and Grzegorz Kondrak. 2024. Semantically-Prompted Language Models Improve Visual Descriptions. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 4285–4302, Mexico City, Mexico. Association for Computational Linguistics.