计算机科学
基础(证据)
人工智能
机器学习
任务(项目管理)
分割
适配器(计算)
工程类
考古
系统工程
历史
操作系统
作者
Feng Chen,Mario Valerio Giuffrida,Sotirios A. Tsaftaris
标识
DOI:10.1109/iccvw60793.2023.00067
摘要
Foundation models are large models pre-trained on tremendous amount of data. They can be typically adapted to diverse downstream tasks with minimal effort. However, as foundation models are usually pre-trained on images or texts sourced from the Internet, their performance in specialized domains, such as plant phenotyping, comes into question. In addition, fully fine-tuning foundation models is time-consuming and requires high computational power. This paper investigates the efficient adaptation of foundation models for plant phenotyping settings and tasks. We perform extensive experiments on fine-tuning three foundation models, MAE, DINO, and DINOv2 on three essential plant phenotyping tasks: leaf counting, instance segmentation, and disease classification. In particular, the pretrained backbones are kept frozen, while two distinct fine-tuning methods are evaluated, namely adapter tuning (using LoRA) and decoder tuning. The experimental results show that a foundation model can be efficiently adapted to multiple plant phenotyping tasks, yielding similar performance as the state-of-the-art (SoTA) models specifically designed or trained for each task. Despite exhibiting great transferability over different tasks, the fine-tuned foundation models perform slightly worse than the SoTA task-specific models in some scenarios, which requires further investigation.
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