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arXiv · 2306.06077

Semantically-Prompted Language Models Improve Visual Descriptions

Michael Ogezi·Bradley Hauer·Grzegorz Kondrak
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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.

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Michael Ogezi, Bradley Hauer, Grzegorz Kondrak. 2023-06-05. Semantically-Prompted Language Models Improve Visual Descriptions. https://doi.org/10.18653/v1%2F2024.findings-naacl.267

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