Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollution

2024/09/05 by Don, Marga, Stijn Pinson, Pinson, Stijn +4 · 1 citation
Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Hydrological Forecasting Using AI

paper · pdf · doi:10.48550/arxiv.2409.03754

openalex publication_date 2024/09/05 · openalex created_date 2024/10/18 · openalex updated_date 2026/07/28

Abstract

Foundation models (FMs) are a popular topic of research in AI. Their ability to generalize to new tasks and datasets without retraining or needing an abundance of data makes them an appealing candidate for applications on specialist datasets. In this work, we compare the performance of FMs to finetuned pre-trained supervised models in the task of semantic segmentation on an entirely new dataset. We see that finetuned models consistently outperform the FMs tested, even in cases were data is scarce. We release the code and dataset for this work on GitHub.

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• REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent

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