GroupCoOp: Group-robust Fine-tuning via Group Prompt Learning
Keywords: spurious correlation, debiasing, group robustness, prompt learning, vision-language models
Abstract: Parameter-efficient fine-tuning (PEFT) of vision-language models (VLMs) excels in various vision tasks thanks to the rich knowledge and generalization ability of VLMs. However, recent studies revealed that such fine-tuned VLMs are vulnerable to spurious correlations stemming from the subgroup imbalance in the fine-tuning datasets. To resolve this issue, we propose Group Context Optimization (GroupCoOp), a simple and effective debiased fine-tuning algorithm that enhances the group robustness of fine-tuned VLMs without group labels. Its key idea is to employ group-specific text prompts as group representatives serving as multiple classifiers for their target class. The rich semantic knowledge of the text encoder of VLM enables the discovery of effective group prompts even for groups with a small number of training samples. Leveraging the group prompts for each class addresses the issues caused by the group-imbalanced training set, such as the neglect of minority groups and the scattered distribution of each class in the embedding space. Moreover, we propose a simple yet fairly effective pseudo group labeling algorithm, which allows GroupCoOp to fine-tune VLMs without manual group labels. GroupCoOp achieved the best results on five benchmarks across five CLIP architectures and even outperformed prior methods that train the entire network, despite training only 0.016\% of the network's parameters. GroupCoOp demonstrates robust performance even with extremely limited training samples, where the minority group sample is limited to a single instance.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 5601
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