用元学习自动快速调优图像分割大模型，三分钟内超越人工调参。
Quickly Tuning Foundation Models for Image Segmentation
- 基于元学习预测最优超参数配置，搜索2亿+组合
- 三分钟内多任务提升零样本分割性能，二分类胜过AutoGluon
- 适合需要快速适配新场景的工业应用与科研人员
像SAM（Segment Anything Model）这样的基础模型虽具备强大的零样本图像分割能力，但在特定领域任务上表现常不足。传统微调需大量人工与专业知识。本文提出QTT-SEG，一种基于元学习的自动化加速微调方法，依托Quick-Tune超参数优化框架，利用元学习建立成本与性能模型，高效探索超过2亿种配置可能性。我们在八组二分类和五组多类别分割数据集上评估，时间严格受限。结果表明，QTT-SEG持续优于SAM的零样本表现，在多数二分类任务中三分钟内超越AutoGluon Multimodal这一强基线；多类别任务亦取得稳定提升。这些发现凸显了元学习在自动化模型适配专业分割任务中的潜力。代码已开源：https://github.com/ds-brx/QTT-SEG/
原文摘要 · Abstract (English)
Foundation models like SAM (Segment Anything Model) exhibit strong zero-shot image segmentation performance, but often fall short on domain-specific tasks. Fine-tuning these models typically requires significant manual effort and domain expertise. In this work, we introduce QTT-SEG, a meta-learning-driven approach for automating and accelerating the fine-tuning of SAM for image segmentation. Built on the Quick-Tune hyperparameter optimization framework, QTT-SEG predicts high-performing configurations using meta-learned cost and performance models, efficiently navigating a search space of over 200 million possibilities. We evaluate QTT-SEG on eight binary and five multiclass segmentation datasets under tight time constraints. Our results show that QTT-SEG consistently improves upon SAM's zero-shot performance and surpasses AutoGluon Multimodal, a strong AutoML baseline, on most binary tasks within three minutes. On multiclass datasets, QTT-SEG delivers consistent gains as well. These findings highlight the promise of meta-learning in automating model adaptation for specialized segmentation tasks. Code available at: https://github.com/ds-brx/QTT-SEG/
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