Long-tail recognition is challenging because it requires the model to learn
good representations from tail categories and address imbalances across all
categories. In this paper, we propose a novel generative and fine-tuning
framework, LTGC, to handle long-tail recognition via leveraging generated
content. Firstly, inspired by the rich implicit knowledge in large-scale models
(e.g., large language models, LLMs), LTGC leverages the power of these models
to parse and reason over the original tail data to produce diverse tail-class
content. We then propose several novel designs for LTGC to ensure the quality
of the generated data and to efficiently fine-tune the model using both the
generated and original data. The visualization demonstrates the effectiveness
of the generation module in LTGC, which produces accurate and diverse tail
data. Additionally, the experimental results demonstrate that our LTGC
outperforms existing state-of-the-art methods on popular long-tailed
benchmarks.

本文提出了一种新的生成和微调框架，LTGC（Long-Tail Generative and Fine-Tuning Framework），通过利用生成的内容来处理长尾识别问题。该框架通过利用大规模模型中丰富的隐式知识来解析和推理原始尾部数据，生成多样的尾部类别内容，并通过几种新颖的设计确保生成数据的质量，以及利用生成和原始数据进行高效微调。可视化结果证明了 LTGC 中生成模块的有效性，它能产生准确且多样的尾部数据，实验结果表明我们的 LTGC 在流行的长尾基准测试中优于现有的最先进方法。