Concepedia

Foundation Model-based Segmentation

Foundation Model-Based Segmentation is an instance segmentation approach that leverages a pre-trained foundation model as its architectural and representational backbone, distinguishing itself from conventional methods by enabling generalization across diverse vision tasks through transfer learning and prompt-based adaptation.

51

Publications

3.6K

Citations

309

Authors

86

Institutions

Publications per year

2017–2026

51

Parents

Publications

51

Micrograph segmentations for DDEVD

Alexander M. Kirillov, Eric Mintun, Nikhila Ravi et al. · arXiv (Cornell University) · 2023 · 543 citations · Full text

, , +9

SAM-Adapter: Adapting Segment Anything in Underperformed Scenes

Tianrun Chen, Lanyun Zhu, Chaotao Ding et al. · 2023 · 315 citations

, , +11

RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation Based on Visual Foundation Model

Keyan Chen, Chenyang Liu, Hao Chen et al. · IEEE Transactions on Geoscience and Remote Sensing · 2024 · 291 citations · Full text

, , +14

How Segment Anything Model (Sam) Boost Medical Image Segmentation: A Survey

Yichi Zhang, Rushi Jiao · SSRN Electronic Journal · 2023 · 227 citations · Full text

, , +10

Foundation Models Defining a New Era in Vision: A Survey and Outlook

Muzammal Naseer, Salman Khan, Rao Muhammad Anwer et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2025 · 192 citations

, , +17

1–5 of 51

Authors

309

  1. ZS
    Zhenwei Shi

    Beihang University, China

    3 publications · 345 citations

    3

    h-index

  2. ZZ
    Zhengxia Zou

    Beihang University, China

    3 publications · 345 citations

    3

    h-index

  3. KC
    Keyan Chen

    Beihang University, China

    3 publications · 345 citations

    3

    h-index

  4. PM
    Papa Mao

    Huzhou University, China

    2 publications · 356 citations

    2

    h-index

  5. MF
    Marei Freitag

    German Research Centre for Artificial Intelligence, Germany

    2 publications · 250 citations

    2

    h-index

1–5 of 309

Institutions

86

  1. University of Hong Kong

    Pok Fu Lam, Hong Kong

    3 h-index

    5 publications

    531 citations

  2. Beihang University

    Beijing, China

    3 h-index

    12 publications

    2K citations

  3. Shanghai Artificial Intelligence Laboratory

    Shanghai, China

    3 h-index

    8 publications

    1.9K citations

  4. University of California, Santa Barbara

    Santa Barbara, United States

    2 h-index

    3 publications

    125 citations

  5. California Institute of Technology

    Pasadena, United States

    2 h-index

    37 publications

    1.4K citations

  6. Chinese Academy of Sciences

    Beijing, China

    2 h-index

    13 publications

    1.3K citations

  7. Beijing Academy of Artificial Intelligence

    Beijing, China

    2 h-index

    7 publications

    1.8K citations

  8. Google (United States)

    Mountain View, United States

    2 h-index

    6 publications

    402 citations

1–8 of 86

Venues

  1. arXiv (Cornell University)

    Repository · 18 publications · 1K citations

    15

    h-index

  2. Nature Methods

    4 publications · 357 citations

    4

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 3 publications · 275 citations

    3

    h-index

  4. 3

    h-index

  5. Remote Sensing

    2 publications · 62 citations

    2

    h-index

1–5