Jonathan Long
Jonathan Long is known for fully convolutional networks, a computer vision method for semantic segmentation. They co-wrote the paper with Evan Shelhamer and Trevor Darrell, which was submitted to arXiv in November 2014 and published at CVPR 2015. A doctorate from UC Berkeley followed in 2016, with Trevor Darrell as advisor. They were also among the core developers of Caffe. In June 2025, the fully convolutional networks paper received the Longuet-Higgins Prize at CVPR.
Timeline
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2014–2015
Fully convolutional networks
They co-write Fully Convolutional Networks for Semantic Segmentation with Evan Shelhamer and Trevor Darrell. The paper is submitted to arXiv in November 2014 and published at CVPR 2015.
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January 2016
Core developers of Caffe
In January 2016, the Embedded Vision Alliance interviews Evan Shelhamer, Jeff Donahue and Jonathan Long, the core Caffe developers at U.C. Berkeley.
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May 2016
A Berkeley doctorate
They complete a doctorate at UC Berkeley in 2016 with a dissertation on convolutional networks for local recognition problems, advised by Trevor Darrell.
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May 2016
A journal edition in PAMI
A journal edition of the paper, with Long among its authors, is accepted by PAMI in May 2016.
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June 2025
The Longuet-Higgins Prize
The fully convolutional networks paper receives the Longuet-Higgins Prize at CVPR in Nashville in June 2025.
Sources and credits
Photo: We have no published photo of Jonathan Long yet. Send us a free-licensed photo.
- [1411.4038] Fully Convolutional Networks for Semantic Segmentation
- CVPR 2015 Open Access Repository
- The Caffe Deep Learning Framework: An Interview with the Core Developers - Edge AI and Vision Alliance
- Understanding and Designing Convolutional Networks for Local Recognition Problems | EECS at UC Berkeley
- [1605.06211] Fully Convolutional Networks for Semantic Segmentation
- IEEE Computer Society Celebrates 2025 Technical Community on Pattern Analysis and Machine Intelligence (TCPAMI) Awardees