Identity
- Type
- university
- Country label
- China
- HQ country
- China
- Founding country
- China
- Parent
- not in the notes
- Parent country
- not in the notes
- Country basis
- the research notes
Where this comes from
Convention D1: a dual label, HQ first, when the HQ, founding or parent country differ.
Models by Class
Models
ModelClassOpennessParametersReleased
Dinomaly2Tsinghua University, Beijing Institute of Technology, Shanghai Jiao Tong University, Fudan Universityvision24 Oct 2025
LIMOreasoningFeb 2025HybrIKvision30 Nov 2020TTSRShanghai Jiao Tong University, Microsoft Researchimage generation7 Jun 2020GCA Mattingvision13 Jan 2020R3Detgeospatial15 Aug 2019BSNvision8 Jun 2018AlphaPosevision17 Jan 2018Links
OrganisationShanghai Jiao Tong Universityuniversity, China
Shanghai Jiao Tong Universityuniversity, China
Models 8
Dinomaly2Tsinghua University, Beijing Institute of Technology, Shanghai Jiao Tong University, Fudan University, visionDeveloperAlphaPoseShanghai Jiao Tong University, visionDeveloperR3DetShanghai Jiao Tong University, geospatialDeveloperTTSRShanghai Jiao Tong University, Microsoft Research, image generationDeveloperBSNShanghai Jiao Tong University, visionDeveloperGCA MattingShanghai Jiao Tong University, visionDeveloperHybrIKShanghai Jiao Tong University, visionDeveloperLIMOShanghai Jiao Tong University, reasoningDeveloperScores
| Benchmark | Model | Score | Who ran it | Setting |
|---|---|---|---|---|
| AIME24 | LIMO | 63.3 % | vendor-reported | |
| MATH500 | LIMO | 95.6 % | vendor-reported | |
| MVTec AD | Dinomaly2 | 99.9 AUROC | vendor-reported | Dinomaly2-L, multi-class unified, image AUROC |
| Real-IAD | Dinomaly2 | 92.1 AUROC | vendor-reported | Dinomaly2-L, multi-class unified, image AUROC |
| VisA | Dinomaly2 | 99.3 AUROC | vendor-reported | Dinomaly2-L, multi-class unified, image AUROC |
Where this comes from
Every recorded score of this organisation's models, with who ran it. Scores from different settings are not directly comparable.
Sources
- arxiv.org/pdf/2510.17611 https://arxiv.org/pdf/2510.17611
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Shanghai Jiao Tong University", https://aurion.semai.id/organisations/shanghai-jiao-tong-university/, records of October 2026.