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Ross Girshick

First author of R-CNN and Detectron; co-author of Segment Anything; formerly at FAIR, Microsoft and Ai2

Ross Girshick's research interests include algorithms for visual perception, representation learning, and the interaction of vision and language. Girshick's site lists research scientist roles at Microsoft Research (2014–2015), FAIR at Facebook/Meta (2015–2023), and the Allen Institute for AI (2023–2024), and notes that Girshick co-founded Vercept in late 2024, a company acquired in early 2026. Girshick is first author of the 2013 R-CNN object detection paper, is listed first in the citation entry for Detectron, FAIR's Apache-2.0 detection system, and appears among the authors of its successor Detectron2. Girshick is also a co-author of the original Segment Anything paper and of SAM 2.123456

At a glance

Current role
None confirmed in the sources
Sector
Independent
Education
UC Berkeley, UChicago
Work listed
3
Honors
5
Sources cited
12
Profile reviewed

Career

Ross Girshick was a postdoctoral researcher at UC Berkeley and then worked as a research scientist at Microsoft Research from 2014 to 2015, at FAIR (Facebook, later Meta) from 2015 to 2023, and at the Allen Institute for AI from 2023 to 2024, and in late 2024 co-founded Vercept, which was acquired in early 2026.1

Research

Girshick's 2013 R-CNN paper with Jeff Donahue, Trevor Darrell and Jitendra Malik brought deep convolutional features to object detection. Girshick followed it with Fast R-CNN in 2015 and co-authored Faster R-CNN, which added region proposal networks, later that year.278

At FAIR, Girshick co-authored Mask R-CNN (2017), which extended detection to instance segmentation, and Focal Loss for Dense Object Detection (2017). Later work moved toward representation learning and segmentation, including Masked Autoencoders Are Scalable Vision Learners (2021), Segment Anything (2023) and SAM 2 (2024).9101156

Open-source work

Girshick is listed first in the citation entry for Detectron, FAIR's Apache-2.0 object detection system written in Python on Caffe2, and among the authors of its successor Detectron2. The original Segment Anything repository also lists Girshick among its authors and releases the model under the Apache 2.0 license.3412

Work and contributions

  • Segment Anything (SAM and SAM 2) Open weights

    Co-author of the Segment Anything paper (2023) and the SAM 2 paper (2024). The original repository lists Girshick among its authors and releases the model under the Apache 2.0 license.5612

  • Detectron and Detectron2 Open-source code

    Listed first in the citation entry of Detectron, FAIR's object detection system written in Python on Caffe2 and released under Apache 2.0, and listed among the authors in the citation entry of the Apache-2.0 Detectron2 library.34

  • R-CNN Published research

    First author, with Jeff Donahue, Trevor Darrell, and Jitendra Malik, of "Rich feature hierarchies for accurate object detection and semantic segmentation" (2013), the R-CNN paper.2

Timeline

  1. Released the R-CNN object detection paper as first author.2
  2. Joined Microsoft Research as a research scientist.1
  3. Joined FAIR at Facebook as a research scientist.1
  4. Released Fast R-CNN.7
  5. Co-authored Faster R-CNN, introducing region proposal networks.8
  6. Co-authored Mask R-CNN.9
  7. Co-authored Masked Autoencoders Are Scalable Vision Learners.11
  8. Joined the Allen Institute for AI as a research scientist.1
  9. Co-authored the Segment Anything paper.5
  10. Co-founded Vercept in late 2024.1

Documented roles

The sources do not confirm a current role; these are the roles they document.

Honors and recognition

  • PAMI Young Researcher Award · IEEE PAMI Technical Committee, 20171
  • PAMI Mark Everingham Prize (2017, 2021 and 2023) · IEEE PAMI Technical Committee, 20231
  • Longuet-Higgins Prize (R-CNN) · IEEE PAMI Technical Committee (CVPR), 20241
  • Helmholtz Prize (Fast R-CNN) · IEEE PAMI Technical Committee (ICCV), 20251
  • NeurIPS Test of Time Award (Faster R-CNN) · Conference on Neural Information Processing Systems (NeurIPS), 20251

Companies in their history

  • Meta 106 people in the wiki
  • Ai2 23 people in the wiki
  • Microsoft 75 people in the wiki

Suggest a correction or removal

Profiles cover public professional work only, and each statement cites a source. If something is out of date, missing or wrong, tell us and link a source. People can also ask for their profile to be corrected or removed.

References

12 sources · profile reviewed Oct 21, 2026 · data updated Oct 21, 2026

  1. Ross Girshick (rbg) — homepageRoss Girshick · Official page · accessed Oct 21, 2026
  2. Rich feature hierarchies for accurate object detection and semantic segmentation (arXiv 1311.2524)arXiv · Paper · published Nov 11, 2013 · accessed Oct 21, 2026
  3. facebookresearch/Detectron README (citation entry and license)Meta (GitHub repository) · Repository · accessed Oct 21, 2026
  4. facebookresearch/detectron2 README (citation entry and license)Meta (GitHub repository) · Repository · accessed Oct 21, 2026
  5. Segment Anything (arXiv 2304.02643)arXiv · Paper · published Apr 5, 2023 · accessed Oct 21, 2026
  6. SAM 2: Segment Anything in Images and Videos (arXiv 2408.00714)arXiv · Paper · published Aug 1, 2024 · accessed Oct 21, 2026
  7. Fast R-CNN (arXiv 1504.08083)arXiv · Paper · published Apr 30, 2015 · accessed Oct 21, 2026
  8. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (arXiv 1506.01497)arXiv · Paper · published Jun 4, 2015 · accessed Oct 21, 2026
  9. Mask R-CNN (arXiv 1703.06870)arXiv · Paper · published Mar 20, 2017 · accessed Oct 21, 2026
  10. Focal Loss for Dense Object Detection (arXiv 1708.02002)arXiv · Paper · published Aug 7, 2017 · accessed Oct 21, 2026
  11. Masked Autoencoders Are Scalable Vision Learners (arXiv 2111.06377)arXiv · Paper · published Nov 11, 2021 · accessed Oct 21, 2026
  12. facebookresearch/segment-anything README (authors and Apache 2.0 license)Meta (GitHub repository) · Repository · accessed Oct 21, 2026