Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning

• URL: http://arxiv.org/abs/2504.11930v1
• Date: Wed, 16 Apr 2025 10:09:45 GMT
• Title: Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning
• Authors: Hairui Ren, Fan Tang, He Zhao, Zixuan Wang, Dandan Guo, Yi Chang,
• Abstract summary: Current pseudo-labeling strategies often struggle with mismatches between semantic and visual information.<n>We introduce a simple yet effective approach called textbfAugmenting Dtextbfiscriminative textbfRichness via Diffusions (AiR)
• Score: 23.129998055266245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning vision-language models (VLMs) with large amounts of unlabeled data has recently garnered significant interest. However, a key challenge remains the lack of high-quality pseudo-labeled data. Current pseudo-labeling strategies often struggle with mismatches between semantic and visual information, leading to sub-optimal performance of unsupervised prompt learning (UPL) methods. In this paper, we introduce a simple yet effective approach called \textbf{A}ugmenting D\textbf{i}scriminative \textbf{R}ichness via Diffusions (AiR), toward learning a richer discriminating way to represent the class comprehensively and thus facilitate classification. Specifically, our approach includes a pseudo-label generation module that leverages high-fidelity synthetic samples to create an auxiliary classifier, which captures richer visual variation, bridging text-image-pair classification to a more robust image-image-pair classification. Additionally, we exploit the diversity of diffusion-based synthetic samples to enhance prompt learning, providing greater information for semantic-visual alignment. Extensive experiments on five public benchmarks, including RESISC45 and Flowers102, and across three learning paradigms-UL, SSL, and TRZSL-demonstrate that AiR achieves substantial and consistent performance improvements over state-of-the-art unsupervised prompt learning methods.

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