GIST: Generating Image-Specific Text for Fine-grained Object Classification

• URL: http://arxiv.org/abs/2307.11315v2
• Date: Fri, 4 Aug 2023 19:36:31 GMT
• Title: GIST: Generating Image-Specific Text for Fine-grained Object Classification
• Authors: Kathleen M. Lewis and Emily Mu and Adrian V. Dalca and John Guttag
• Abstract summary: GIST is a method for generating image-specific fine-grained text descriptions from image-only datasets. Our method achieves an average improvement of $4.1%$ in accuracy over CLIP linear probes.
• Score: 8.118079247462425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent vision-language models outperform vision-only models on many image classification tasks. However, because of the absence of paired text/image descriptions, it remains difficult to fine-tune these models for fine-grained image classification. In this work, we propose a method, GIST, for generating image-specific fine-grained text descriptions from image-only datasets, and show that these text descriptions can be used to improve classification. Key parts of our method include 1. prompting a pretrained large language model with domain-specific prompts to generate diverse fine-grained text descriptions for each class and 2. using a pretrained vision-language model to match each image to label-preserving text descriptions that capture relevant visual features in the image. We demonstrate the utility of GIST by fine-tuning vision-language models on the image-and-generated-text pairs to learn an aligned vision-language representation space for improved classification. We evaluate our learned representation space in full-shot and few-shot scenarios across four diverse fine-grained classification datasets, each from a different domain. Our method achieves an average improvement of $4.1\%$ in accuracy over CLIP linear probes and an average of $1.1\%$ improvement in accuracy over the previous state-of-the-art image-text classification method on the full-shot datasets. Our method achieves similar improvements across few-shot regimes. Code is available at https://github.com/emu1729/GIST.

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