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ChatGPT-Powered Hierarchical Comparisons for Image Classification

Computer Vision and Pattern Recognition 2023-11-02 v1 Artificial Intelligence

Abstract

The zero-shot open-vocabulary challenge in image classification is tackled by pretrained vision-language models like CLIP, which benefit from incorporating class-specific knowledge from large language models (LLMs) like ChatGPT. However, biases in CLIP lead to similar descriptions for distinct but related classes, prompting our novel image classification framework via hierarchical comparisons: using LLMs to recursively group classes into hierarchies and classifying images by comparing image-text embeddings at each hierarchy level, resulting in an intuitive, effective, and explainable approach.

Keywords

Cite

@article{arxiv.2311.00206,
  title  = {ChatGPT-Powered Hierarchical Comparisons for Image Classification},
  author = {Zhiyuan Ren and Yiyang Su and Xiaoming Liu},
  journal= {arXiv preprint arXiv:2311.00206},
  year   = {2023}
}

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Neurips 2023 Poster

R2 v1 2026-06-28T13:08:04.312Z