Unlabeled Data Improves Fine-Grained Image Zero-shot Classification with Multimodal LLMs

• URL: http://arxiv.org/abs/2506.03195v1
• Date: Sun, 01 Jun 2025 09:04:07 GMT
• Title: Unlabeled Data Improves Fine-Grained Image Zero-shot Classification with Multimodal LLMs
• Authors: Yunqi Hong, Sohyun An, Andrew Bai, Neil Y. C. Lin, Cho-Jui Hsieh,
• Abstract summary: AutoSEP is a self-supervised prompt learning framework designed to enhance fine-grained classification capabilities.<n>Our core idea is to leverage unlabeled data to learn a description prompt that guides MLLMs in identifying crucial discriminative features.<n>AutoSEP on average improves 13 percent over standard zero-shot classification and 5 percent over the best-performing baselines.
• Score: 44.21486904657393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite Multimodal Large Language Models (MLLMs) showing promising results on general zero-shot image classification tasks, fine-grained image classification remains challenging. It demands precise attention to subtle visual details to distinguish between visually similar subcategories--details that MLLMs may easily overlook without explicit guidance. To address this, we introduce AutoSEP, an iterative self-supervised prompt learning framework designed to enhance MLLM fine-grained classification capabilities in a fully unsupervised manner. Our core idea is to leverage unlabeled data to learn a description prompt that guides MLLMs in identifying crucial discriminative features within an image, and boosts classification accuracy. We developed an automatic self-enhancing prompt learning framework called AutoSEP to iteratively improve the description prompt using unlabeled data, based on instance-level classification scoring function. AutoSEP only requires black-box access to MLLMs, eliminating the need for any training or fine-tuning. We evaluate our approach on multiple fine-grained classification datasets. It consistently outperforms other unsupervised baselines, demonstrating the effectiveness of our self-supervised optimization framework. Notably, AutoSEP on average improves 13 percent over standard zero-shot classification and 5 percent over the best-performing baselines. Code is available at: https://github.com/yq-hong/AutoSEP

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