Papers › Meta-Learning Initializations for Image Segmentation

Meta-Learning Initializations for Image Segmentation

13 Dec 2019arXiv:1912.06290archive 2025-07-28

Sean M. Hendryx, Andrew B. Leach, Paul D. Hein, Clayton T. Morrison

We extend first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, present a novel neural network architecture built for fast learning which we call EfficientLab, and leverage a formal definition of the test error of meta-learning algorithms to decrease error on out of distribution tasks. We show state of the art results on the FSS-1000 dataset by meta-training EfficientLab with FOMAML and using Bayesian optimization to infer the optimal test-time adaptation routine hyperparameters. We also construct a small benchmark dataset, FP-k, for the empirical study of how meta-learning systems perform in both few- and many-shot settings. On the FP-k dataset, we show that meta-learned initializations provide value for canonical few-shot image segmentation but their performance is quickly matched by conventional transfer learning with performance being equal beyond 10 labeled examples. Our code, meta-learned model, and the FP-k dataset are available at https://github.com/ml4ai/mliis .

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Tasks

Bayesian Optimization Few-Shot Image Classification Few-Shot Learning Image Classification Image Segmentation Meta-Learning Segmentation Semantic Segmentation Test-time Adaptation Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Few-Shot Semantic Segmentation	FSS-1000	EfficientLab	Mean IoU	82.78	#3 of 3	Archive leaderboard	report

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