In recent years, the rapid evolution of computer vision has seen the
emergence of various vision foundation models, each tailored to specific data
types and tasks. While large language models often share a common pretext task,
the diversity in vision foundation models arises from their varying training
objectives. In this study, we delve into the quest for identifying the most
effective vision foundation models for few-shot semantic segmentation, a
critical task in computer vision. Specifically, we conduct a comprehensive
comparative analysis of four prominent foundation models: DINO V2, Segment
Anything, CLIP, Masked AutoEncoders, and a straightforward ResNet50 pre-trained
on the COCO dataset. Our investigation focuses on their adaptability to new
semantic segmentation tasks, leveraging only a limited number of segmented
images. Our experimental findings reveal that DINO V2 consistently outperforms
the other considered foundation models across a diverse range of datasets and
adaptation methods. This outcome underscores DINO V2's superior capability to
adapt to semantic segmentation tasks compared to its counterparts. Furthermore,
our observations indicate that various adapter methods exhibit similar
performance, emphasizing the paramount importance of selecting a robust feature
extractor over the intricacies of the adaptation technique itself. This insight
sheds light on the critical role of feature extraction in the context of
few-shot semantic segmentation. This research not only contributes valuable
insights into the comparative performance of vision foundation models in the
realm of few-shot semantic segmentation but also highlights the significance of
a robust feature extractor in this domain.

在计算机视觉中，我们比较了四种著名的视觉基础模型（DINO V2，Segment Anything，CLIP，Masked AutoEncoders 和在 COCO 数据集上预训练的 ResNet50），发现 DINO V2 在各种数据集和适应方法上始终优于其他模型，突出了其在语义分割任务上的优越适应能力。此外，我们观察到各种适配器方法表现相似，强调了选择稳健特征提取器比适应技术本身的复杂性更为重要，这一发现揭示了在少样本语义分割情境中特征提取的关键作用。此研究不仅为少样本语义分割领域的视觉基础模型的比较性能提供宝贵见解，还强调了稳健特征提取器在该领域的重要性。