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Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering
Not provided in the abstract
meta-learningfew-shot learningcomputer visionclustering
2609.10749
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4h ago80%
Abstract
This paper presents a few-shot regression framework for plant growth estimation that utilizes Vision Transformers and fuzzy clustering to efficiently learn from limited labeled data.
Reality Card
Core Claim
The proposed framework significantly improves plant growth estimation performance using meta-learning techniques in scenarios with limited labeled data.
Method / Result
Second-order meta-learning methods like MAML++ outperform classical baselines in few-shot learning scenarios.
Limitations
The impact of intra-cluster support selection is limited and varies depending on the dataset.
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