Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollution
Marga Don Stijn Pinson Blanca Guillen Cebrian Yuki M. Asano


 

Evaluation of few-shot semantic segmentation for river pollution



M. Don et al.

University of Amsterdam, Amsterdam, The Netherlands The Ocean Cleanup, Coolsingel 6 Rotterdam, The Netherlands Currently at Technical University of Nuremberg

marga.don@student.uva.nl, y.m.asano@uva.nl, 
 {stijn.pinson, b.guillencebrian}@theoceancleanup.com


Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollution
    Marga Don10009-0001-5435-8935 
Stijn Pinson 2 Blanca Guillen Cebrian 2
Yuki M. Asano 1,3 


    September 9, 2024
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§ 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 https://github.com/TheOceanCleanup/RiverTrashSegmentationhere.
  


content/1_intro
content/2_relatedwork
content/3_dataset
content/4_methods
content/5_experiments
content/6_discussion
content/7_conclusion


splncs04


content/99_supplementary
