all AI news
SAMDA: Leveraging SAM on Few-Shot Domain Adaptation for Electronic Microscopy Segmentation
March 14, 2024, 4:42 a.m. | Yiran Wang, Li Xiao
cs.LG updates on arXiv.org arxiv.org
Abstract: It has been shown that traditional deep learning methods for electronic microscopy segmentation usually suffer from low transferability when samples and annotations are limited, while large-scale vision foundation models are more robust when transferring between different domains but facing sub-optimal improvement under fine-tuning. In this work, we present a new few-shot domain adaptation framework SAMDA, which combines the Segment Anything Model(SAM) with nnUNet in the embedding space to achieve high transferability and accuracy. Specifically, we …
abstract annotations arxiv cs.cv cs.lg deep learning domain domain adaptation domains eess.iv electronic few-shot fine-tuning foundation improvement low microscopy robust sam samples scale segmentation type vision
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Machine Learning Engineer - Sr. Consultant level
@ Visa | Bellevue, WA, United States