GroupCoOp: Group-robust Fine-tuning via Group Prompt Learning

• URL: http://arxiv.org/abs/2509.23781v1
• Date: Sun, 28 Sep 2025 09:54:30 GMT
• Title: GroupCoOp: Group-robust Fine-tuning via Group Prompt Learning
• Authors: Nayeong Kim, Seong Joon Oh, Suha Kwak,
• Abstract summary: Group Context Optimization (GroupCoOp) is a simple and effective debiased fine-tuning algorithm.<n>It enhances the group robustness of fine-tuned vision-language models (VLMs)<n>GroupCoOp achieved the best results on five benchmarks across five CLIP architectures.
• Score: 57.888537648437115
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. 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. GroupCoOp achieved the best results on five benchmarks across five CLIP architectures and occasionally outperformed prior methods that fine-tune the entire network, despite training only 0.016\% of the network's parameters.

Related papers

• The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations [2.6470894980840525]
  Group Recommender Systems (GRS) often used social choice-based aggregation strategies to derive a single recommendation.<n>We investigate under which conditions language models can perform these strategies correctly based on zero-shot learning.<n>We show that performance starts to deteriorate when considering more than 100 ratings.<n>We conclude that future research should include group complexity as a factor in GRS evaluation.
  arXiv  Detail & Related papers  (2025-05-08T07:43:01Z)
• Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness [61.45587642780908]
  We propose a three-step approach for parameter-efficient fine-tuning of image-text foundation models.<n>Our method improves its two key components: minority samples identification and the robust training algorithm.<n>Our theoretical analysis shows that our PPA enhances minority group identification and is Bayes optimal for minimizing the balanced group error.
  arXiv  Detail & Related papers  (2025-03-12T15:46:12Z)
• Take One Gram of Neural Features, Get Enhanced Group Robustness [23.541213868620837]
  Predictive performance of machine learning models trained with empirical risk minimization can degrade considerably under distribution shifts. We propose to partition the training dataset into groups based on Gram matrices of features extracted by an identification'' model. Our approach not only improves group robustness over ERM but also outperforms all recent baselines.
  arXiv  Detail & Related papers  (2022-08-26T12:34:55Z)
• Improved Group Robustness via Classifier Retraining on Independent Splits [6.930560177764658]
  Group distributionally robust optimization is a widely used baseline for learning models with strong worst-group performance. This paper designs a simple method based on the idea of retraining on independent splits of the training data. We find that using a novel sample-splitting procedure achieves robust worst-group performance in the fine-tuning step.
  arXiv  Detail & Related papers  (2022-04-20T16:22:27Z)
• Towards Group Robustness in the presence of Partial Group Labels [61.33713547766866]
  spurious correlations between input samples and the target labels wrongly direct the neural network predictions. We propose an algorithm that optimize for the worst-off group assignments from a constraint set. We show improvements in the minority group's performance while preserving overall aggregate accuracy across groups.
  arXiv  Detail & Related papers  (2022-01-10T22:04:48Z)
• BARACK: Partially Supervised Group Robustness With Guarantees [29.427365308680717]
  We propose BARACK, a framework to improve worst-group performance on neural networks. We train a model to predict the missing group labels for the training data, and then use these predicted group labels in a robust optimization objective. Empirically, our method outperforms the baselines that do not use group information, even when only 1-33% of points have group labels.
  arXiv  Detail & Related papers  (2021-12-31T23:05:21Z)
• Focus on the Common Good: Group Distributional Robustness Follows [47.62596240492509]
  This paper proposes a new and simple algorithm that explicitly encourages learning of features that are shared across various groups. While Group-DRO focuses on groups with worst regularized loss, focusing instead, on groups that enable better performance even on other groups, could lead to learning of shared/common features.
  arXiv  Detail & Related papers  (2021-10-06T09:47:41Z)
• Just Train Twice: Improving Group Robustness without Training Group Information [101.84574184298006]
  Standard training via empirical risk minimization can produce models that achieve high accuracy on average but low accuracy on certain groups. Prior approaches that achieve high worst-group accuracy, like group distributionally robust optimization (group DRO) require expensive group annotations for each training point. We propose a simple two-stage approach, JTT, that first trains a standard ERM model for several epochs, and then trains a second model that upweights the training examples that the first model misclassified.
  arXiv  Detail & Related papers  (2021-07-19T17:52:32Z)
• You Never Cluster Alone [150.94921340034688]
  We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation. We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
  arXiv  Detail & Related papers  (2021-06-03T14:59:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.