在河流污染语义分割任务中，微调模型优于大模型，即使数据极少也更有效。
Foundation Model or Finetune? Evaluation of few-shot semantic segmentation for river pollution
- 对比大模型与微调模型在新数据集上的表现
- 微调模型在少样本情况下仍显著优于大模型
- 适合关注小样本场景下模型性能的从业者
基础模型（FMs）是人工智能领域的热门研究方向，其无需重新训练或大量数据即可泛化到新任务和数据集的能力使其成为专业数据集应用的理想候选。本文在全新数据集上比较了基础模型与微调预训练监督模型在语义分割任务中的表现。结果表明，即使在数据稀缺的情况下，微调模型也始终优于所测试的基础模型。相关代码和数据集已发布于GitHub。
原文摘要 · Abstract (English)
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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