A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models
CoRR(2024)
摘要
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.
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