基于视觉Transformer与模糊聚类的数据高效植物生长估计元学习
Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering
- Norwegian University of Life Sciences (NMBU)（挪威生命科学大学）
- Photosynthetic AS（Photosynthetic 公司）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出结合ViT嵌入、模糊聚类任务构建与梯度元学习的少样本回归框架，实现标签稀缺下可靠的植物生长估计。
中文摘要 AI 辅助
准确的植物生长估计对于温室监测至关重要，然而获取标注数据既昂贵又耗时。为解决这一问题，我们提出了一种少样本回归框架，该框架结合了视觉Transformer（ViT）特征嵌入、基于聚类的任务构建和基于梯度的元学习，并表明嵌入空间中的任务构建是性能的主要驱动力。该方法利用未标记图像池，通过模糊c均值聚类将数据组织成结构化任务，从而能够从少量标注样本中进行高效学习。我们系统地评估了元学习方法，并表明在少样本场景下，二阶方法（例如模型无关元学习变体如MAML++）优于经典基线。此外，簇内支持集选择的影响有限且依赖于数据集。在两个植物数据集上的实验表明，结构化任务设计与元学习相结合，能够在严重标签稀缺的情况下实现可靠的植物生长估计。
英文摘要
Accurate plant growth estimation is essential for greenhouse monitoring, yet obtaining labeled data remains costly and time-consuming. To address this, we propose a few-shot regression framework that combines Vision Transformer (ViT) feature embeddings, clustering-based task construction, and gradient-based meta-learning, and show that task construction in embedding space is a primary driver of performance. The approach leverages an unlabeled image pool to organize data into structured tasks using fuzzy c-means clustering, enabling efficient learning from a small number of labeled samples. We systematically evaluate meta-learning methods and show that second-order methods (e.g., Model-Agnostic Meta-Learning variants such as MAML++) outperform classical baselines in the few-shot regime. Furthermore, intra-cluster support selection has a limited and dataset-dependent impact. Experiments on two plant datasets show that structured task design combined with meta-learning enables reliable plant growth estimation under severe label scarcity.
