LLM-Assisted Entropy-Based Adaptive Distillation for Unsupervised Fine-Grained Visual Representation Learning

Unsupervised Fine-grained Visual Represent Learning (FVRL) aims to learn discriminative features to distinguish subtle differences among visually similar categories without using labeled fine-grained data. Existing works, which typically learn representation from target data, often struggle to capture subtle inter-class variations due to the limited prior fine-grained knowledge. To alleviate it, this paper proposes LLM-assisted Entropy-based Adaptive Distillation (LEAD), a novel unsupervised FVRL framework that selectively distills fine-grained knowledge from a powerful teacher model built upon pre-trained models. Specifically, we first harness the powerful reasoning capabilities of Large Language Models (LLMs) to generate contextual knowledge of fine-grained category-aware descriptions, enriching semantic priors in the teacher model. These descriptions are then used to form a prototype-driven finegrained classifier, which acts as an assistant to generate rich knowledge with a frozen vision-language model. Besides, to achieve effective knowledge transfer, we further introduce an entropy-based adaptive mechanism, which dynamically adjusts the distillation strength based on the information entropy to identify and prioritize valuable knowledge. Extensive experimental results on three fine-grained datasets demonstrate the effectiveness and efficiency of our proposed LEAD for unsupervised FVRL.