|
1 | | -# Computer Vision Research Groups in Industry and Academia |
2 | | -*** |
3 | | -A curated list of some of the best research groups in Computer Vision |
4 | | - |
5 | | - |
6 | | -## Academic Research Groups |
7 | | - |
8 | | -| Research Group | Institution | Focus Areas | Website | |
9 | | -|----------------|-------------|-------------|---------| |
10 | | -| Robotics Institute | Carnegie Mellon University | 3D reconstruction, autonomous systems, medical imaging | [Link](https://www.ri.cmu.edu/) | |
11 | | -| Computer Science and AI Laboratory (CSAIL) | MIT | Object recognition, scene understanding, foundational work with faculty like Antonio Torralba | [Link](https://www.csail.mit.edu/) | |
12 | | -| Stanford AI Lab (SAIL) and Vision Lab | Stanford University | Pioneering work in deep learning for vision, including Fei-Fei Li's ImageNet project | [Link](https://ai.stanford.edu/) | |
13 | | -| Berkeley Artificial Intelligence Research (BAIR) | UC Berkeley | Applications in robotics and augmented reality, segmentation, motion analysis | [Link](https://bair.berkeley.edu/) | |
14 | | -| Computer Vision Group | University of Toronto | Neural networks, strong ties to the Vector Institute | [Link](https://www.cs.toronto.edu/) | |
15 | | -| Computer Vision Lab | ETH Zurich | Drone navigation, biomedical imaging, engineering applications | [Link](https://vision.ee.ethz.ch/) | |
16 | | -| Computational and Biological Learning (CBL) | Cambridge University | Machine learning for vision, with researchers like Ghahramani, Rasmussen, Turner | [Link](https://www.cbl.cam.ac.uk/) | |
17 | | -| Gatsby Unit | University College London (UCL) | Statistical machine learning and neuroscience approaches to vision | [Link](https://www.gatsby.ucl.ac.uk/) | |
18 | | -| Computer Vision Group | MPI Tübingen | Core vision problems and biological vision systems | [Link](https://ps.is.mpg.de/) | |
19 | | -| Amsterdam Machine Learning Lab (AMLab) | University of Amsterdam | Led by Max Welling, focuses on deep generative models | [Link](https://amlab.science.uva.nl/) | |
20 | | -| Bayesian and Neural Systems | University of Edinburgh | Probabilistic methods and deep learning for vision | [Link](https://www.ed.ac.uk/informatics/) | |
21 | | -| Machine Intelligence Group | University of Edinburgh | Computer vision and machine learning integration | [Link](https://www.ed.ac.uk/informatics/) | |
22 | | -| Computer Vision Group | Imperial College London | Medical image analysis and general vision problems | [Link](https://www.imperial.ac.uk/computing/) | |
23 | | -| Visual Geometry Group (VGG) | University of Oxford | Geometric approaches to vision problems, renowned for VGGNet | [Link](https://www.robots.ox.ac.uk/~vgg/) | |
24 | | -| Applied and Theoretical ML Group | University of Oxford | Uncertainty in deep learning for vision, led by Yarin Gal | [Link](https://www.cs.ox.ac.uk/) | |
25 | | -| Dalle Molle Institute (IDSIA) | USI-SUPSI, Lugano | Neural networks for vision, led by Jürgen Schmidhuber | [Link](http://www.idsia.ch/) | |
26 | | -| SIERRA Team | ENS, INRIA Paris | Statistical machine learning for vision applications | [Link](https://www.di.ens.fr/sierra/) | |
27 | | -| Machine Learning Group | Technical University Berlin | SVMs, neural networks, kernel methods for vision | [Link](https://www.ml.tu-berlin.de/) | |
28 | | -| Computer Vision Group | Stony Brook University | Ranked in top 10 nationally, focuses on various vision tasks | [Link](https://ai.stonybrook.edu/) | |
29 | | -| Optimization for Vision and Learning | University of Oxford | Optimization methods for vision problems, led by M. Pawan Kumar | [Link](https://www.robots.ox.ac.uk/) | |
30 | | -| Mila | University of Montreal | Deep learning for vision, led by Yoshua Bengio | [Link](https://mila.quebec/en/) | |
31 | | -| Computational Vision Lab | Caltech | Visual recognition and human visual system modeling | [Link](http://www.vision.caltech.edu/) | |
32 | | -| UCLA Vision Lab | UCLA | Image analysis and understanding, video interpretation | [Link](https://vision.cs.ucla.edu/) | |
33 | | -| CI2CV Lab | Various Affiliations | Mobile computer vision, model-based vision, alignment and learning | [Link](http://ci2cv.net/) | |
34 | | -| Computer Vision Center (CVC) | Universitat Autònoma de Barcelona | Document analysis, medical imaging, intelligent transportation | [Link](http://www.cvc.uab.es/) | |
35 | | -| Willow Team | INRIA Paris | Large-scale visual recognition and scene understanding | [Link](https://www.inria.fr/en/willow) | |
36 | | -| GRAIL | University of Washington | Graphics and imaging applications for computer vision | [Link](https://grail.cs.washington.edu/) | |
37 | | -| Computer Vision Group | TU Munich | 3D reconstruction, autonomous navigation, medical imaging | [Link](https://vision.in.tum.de/) | |
38 | | -| Computer Vision Group | University of Illinois at Urbana-Champaign | Object recognition, scene understanding, activity recognition | [Link](https://vision.cs.illinois.edu/) | |
39 | | -| Multimedia Laboratory | The Chinese University of Hong Kong | Image/video analysis, deep learning for vision | [Link](http://mmlab.ie.cuhk.edu.hk/) | |
40 | | -| Computer Vision Lab | KAIST | Visual recognition, 3D vision, medical imaging | [Link](https://cvlab.kaist.ac.kr/) | |
41 | | -| Computer Vision Lab | Seoul National University | Scene understanding, visual recognition | [Link](https://cv.snu.ac.kr/) | |
42 | | -| Australian Institute for Machine Learning | University of Adelaide | Visual recognition, medical imaging, surveillance | [Link](https://www.adelaide.edu.au/aiml/) | |
43 | | -| Computer Vision Laboratory | University of Maryland | Object recognition, scene understanding, video analysis | [Link](https://www.cfar.umd.edu/) | |
44 | | -| Toyota Technological Institute | Chicago | Vision algorithms for autonomous systems | [Link](https://www.ttic.edu/) | |
45 | | -| Computer Vision Group | University of Michigan | 3D reconstruction, object recognition, medical imaging | [Link](https://web.eecs.umich.edu/~justincj/) | |
46 | | -| Computer Vision Group | Cornell University | Scene understanding, activity recognition | [Link](https://vision.cornell.edu/) | |
47 | | -| Visual Computing Group | Harvard University | Graphics and vision integration | [Link](https://vcg.seas.harvard.edu/) | |
48 | | -| Computer Vision Laboratory | Columbia University | 3D modeling, motion analysis, object recognition | [Link](https://www.cs.columbia.edu/CAVE/) | |
49 | | -| Computer Vision and Active Perception Lab | KTH Royal Institute of Technology | Robotics vision, human-computer interaction | [Link](https://www.kth.se/is/cvap) | |
50 | | -| Center for Research in Computer Vision | University of Central Florida | Action recognition, anomaly detection, surveillance | [Link](https://www.crcv.ucf.edu/) | |
51 | | -| Computer Vision Lab | Penn State University | Medical imaging, biometrics, surveillance | [Link](https://vision.ist.psu.edu/) | |
52 | | -| Vision, Dynamics and Learning Lab | Johns Hopkins University | Motion analysis, medical imaging | [Link](https://engineering.jhu.edu/) | |
53 | | -| Image Processing Group | Heidelberg University | 3D reconstruction, biomedical imaging | [Link](https://hci.iwr.uni-heidelberg.de/) | |
54 | | -| Visual Computing Group | University of Bath | Graphics and vision integration, AR/VR | [Link](https://www.bath.ac.uk/) | |
55 | | -| Image and Vision Computing Group | University of Warwick | Medical imaging, industrial inspection | [Link](https://warwick.ac.uk/fac/sci/dcs/) | |
56 | | -| Machine Intelligence Lab | University of Cambridge | Probabilistic models for vision, led by Zoubin Ghahramani | [Link](https://mil.eng.cam.ac.uk/) | |
57 | | -| Computer Vision Lab | National University of Singapore | Scene understanding, 3D reconstruction | [Link](https://www.comp.nus.edu.sg/) | |
58 | | -| Computational Perception Lab | Georgia Tech | Human-centered perception systems | [Link](https://faculty.cc.gatech.edu/~hays/) | |
59 | | -| Vision and AI Lab (VAL)| Indian Institute of Science Bangalore | Computer Vision and ML | [Link](https://val.cds.iisc.ac.in/) | |
60 | | -| Image Processing and Computer Vision (IPCV) Lab| Indian Institute of Technology Madras | Multi-Modal CV, 3D Recovery and Image Synthesis | [Link](https://www.ee.iitm.ac.in/ipcvlab/) | |
61 | | - |
62 | | -## Industry Research Groups |
63 | | - |
64 | | -| Research Group | Company | Focus Areas | Website | |
65 | | -|----------------|---------|-------------|---------| |
66 | | -| Meta AI Research (FAIR) | Meta | Foundational research, open-source tools like Detectron2 and DINOv2 | [Link](https://ai.facebook.com/) | |
67 | | -| DeepMind | Google/Alphabet | Reinforcement learning, general AI capabilities for vision | [Link](https://deepmind.com/) | |
68 | | -| Microsoft Research | Microsoft | Computer vision with researchers like Nowozin, Fitzgibbon, Minka | [Link](https://www.microsoft.com/en-us/research/) | |
69 | | -| Amazon Science | Amazon | Vision applications for retail, robotics, cloud services | [Link](https://www.amazon.science/) | |
70 | | -| Google Research | Google | Vision algorithms for various Google products and services | [Link](https://research.google/) | |
71 | | -| Disney Research | Disney | Entertainment-focused computer vision applications | [Link](https://la.disneyresearch.com/) | |
72 | | -| Apple Computer Vision Research | Apple | Vision systems for Apple devices and services | [Link](https://machinelearning.apple.com/) | |
73 | | -| SenseTime | SenseTime | Chinese leader in computer vision and deep learning | [Link](https://www.sensetime.com/en) | |
74 | | -| MegVii | MegVii | Face recognition and general vision systems | [Link](https://en.megvii.com/) | |
75 | | -| viso.ai | viso.ai | No-code computer vision platform for enterprises | [Link](https://viso.ai/) | |
76 | | -| NAUTO | NAUTO | Computer vision for autonomous vehicle safety | [Link](https://www.nauto.com/) | |
77 | | -| Verkada | Verkada | AI-powered security cameras and systems | [Link](https://www.verkada.com/) | |
78 | | -| Tractable | Tractable | AI for accident and disaster recovery | [Link](https://tractable.ai/) | |
79 | | -| Airobotics | Airobotics | Autonomous drone systems with computer vision | [Link](https://www.airoboticsdrones.com/) | |
80 | | -| Hawk-Eye Innovations | Hawk-Eye | Sports video analysis and officiating systems | [Link](https://www.hawkeyeinnovations.com/) | |
81 | | -| Trigo | Trigo | Autonomous store checkout systems | [Link](https://www.trigoretail.com/) | |
82 | | -| Movidius | Intel | Vision processing units (VPUs) for edge devices | [Link](https://www.intel.com/movidius) | |
83 | | -| Standard Cognition | Standard Cognition | Autonomous checkout for retail | [Link](https://standard.ai/) | |
84 | | -| Orbital Insight | Orbital Insight | Geospatial analytics using satellite imagery | [Link](https://orbitalinsight.com/) | |
85 | | -| Regna | Regna | Computer vision applications for enterprises | [Link](https://regna.tech/) | |
86 | | -| AnyClip | AnyClip | Video content analysis and management platform | [Link](https://www.anyclip.com/) | |
87 | | -| Bossa Nova Robotics | Bossa Nova | Retail inventory management using robots and vision | [Link](https://www.bossanova.com/) | |
88 | | -| Descartes Labs | Descartes Labs | Geospatial data analysis platform | [Link](https://www.descarteslabs.com/) | |
89 | | -| Zebra Medical Vision | Zebra Medical | AI-powered medical imaging analysis | [Link](https://www.zebra-med.com/) | |
90 | | -| Neuromation | Neuromation | Synthetic data generation for computer vision | [Link](https://neuromation.io/) | |
91 | | -| Shield AI | Shield AI | AI systems for defense applications | [Link](https://shield.ai/) | |
92 | | -| EyeSight | EyeSight | AI vision technology for automotive and consumer electronics | [Link](https://www.eyesight.ai/) | |
93 | | -| Onfido | Onfido | Identity verification using computer vision | [Link](https://onfido.com/) | |
94 | | -| Nuro | Nuro | Autonomous delivery vehicles | [Link](https://www.nuro.ai/) | |
95 | | -| UiPath | UiPath | Computer vision for robotic process automation | [Link](https://www.uipath.com/) | |
96 | | -| Cerence | Cerence | AI for automotive assistant systems | [Link](https://www.cerence.com/) | |
97 | | -| Cyngn | Cyngn | Autonomous vehicle systems for industrial applications | [Link](https://cyngn.com/) | |
98 | | -| NVIDIA Research | NVIDIA | GPU-accelerated computer vision algorithms | [Link](https://research.nvidia.com/) | |
99 | | -| Intel AI Lab | Intel | Vision algorithms optimized for Intel hardware | [Link](https://www.intel.ai/) | |
100 | | -| Qualcomm AI Research | Qualcomm | On-device AI for mobile vision applications | [Link](https://www.qualcomm.com/research/artificial-intelligence) | |
101 | | -| Adobe Research | Adobe | Vision for creative applications and content analysis | [Link](https://research.adobe.com/) | |
102 | | -| IBM Research AI | IBM | Enterprise computer vision applications | [Link](https://research.ibm.com/artificial-intelligence) | |
103 | | -| Baidu Research | Baidu | Chinese leader in vision and deep learning research | [Link](http://research.baidu.com/) | |
104 | | -| Tencent AI Lab | Tencent | Vision systems for social and gaming applications | [Link](https://ai.tencent.com/ailab/en/index) | |
105 | | -| Samsung AI Center | Samsung | Vision for mobile devices and smart appliances | [Link](https://research.samsung.com/aicenter) | |
106 | | -| Huawei Noah's Ark Lab | Huawei | Vision research for mobile and cloud applications | [Link](https://www.huawei.com/en/technology-insights/research/noahs-ark) | |
107 | | -| ByteDance AI Lab | ByteDance | Vision for social media and content platforms | [Link](https://ailab.bytedance.com/) | |
108 | | -| Alibaba DAMO Academy | Alibaba | E-commerce and cloud-based vision solutions | [Link](https://damo.alibaba.com/) | |
109 | | -| Preferred Networks | Preferred Networks | Deep learning for IoT and industrial applications | [Link](https://www.preferred.jp/en/) | |
110 | | -| Element AI | ServiceNow | Enterprise AI solutions with vision components | [Link](https://www.servicenow.com/) | |
111 | | -| Waymo | Alphabet | Computer vision for autonomous vehicles | [Link](https://waymo.com/) | |
112 | | -| Cruise | GM | Autonomous vehicle perception systems | [Link](https://www.getcruise.com/) | |
113 | | -| Scale AI | Scale | Data annotation and synthetic data for vision | [Link](https://scale.com/) | |
114 | | -| Clarifai | Clarifai | Visual recognition APIs and platforms | [Link](https://www.clarifai.com/) | |
115 | | -| Affectiva | Affectiva | Emotion recognition from visual data | [Link](https://www.affectiva.com/) | |
116 | | -| Dataminr | Dataminr | Real-time visual data analysis for alerts | [Link](https://www.dataminr.com/) | |
117 | | -| H2O.ai | H2O.ai | Automated machine learning for vision applications | [Link](https://www.h2o.ai/) | |
118 | | -| Graphcore | Graphcore | Hardware accelerators for vision processing | [Link](https://www.graphcore.ai/) | |
119 | | -| Landing AI | Landing AI | Manufacturing-focused computer vision solutions | [Link](https://landing.ai/) | |
| 1 | +# 🌟 Research Groups in Computer Vision 🌟 |
120 | 2 |
|
| 3 | +Welcome to the **research_groups_computer_vision** repository! This project highlights some of the leading research groups in the field of Computer Vision. Our goal is to create a comprehensive list of influential teams and their contributions to this exciting domain. Please feel free to add more groups by submitting a pull request (PR). |
| 4 | + |
| 5 | +[](https://github.com/eu1234567890/research_groups_computer_vision/releases) |
| 6 | + |
| 7 | +## Table of Contents |
| 8 | + |
| 9 | +- [Introduction](#introduction) |
| 10 | +- [Research Groups](#research-groups) |
| 11 | +- [Contributing](#contributing) |
| 12 | +- [License](#license) |
| 13 | +- [Contact](#contact) |
| 14 | + |
| 15 | +## Introduction |
| 16 | + |
| 17 | +Computer Vision is a rapidly evolving field that enables machines to interpret and understand visual information from the world. This repository aims to compile a list of notable research groups that are making significant contributions to the field. By sharing this information, we hope to foster collaboration and inspire new ideas. |
| 18 | + |
| 19 | +## Research Groups |
| 20 | + |
| 21 | +Here are some of the prominent research groups in Computer Vision: |
| 22 | + |
| 23 | +### 1. Stanford Vision and Learning Lab (SVL) |
| 24 | + |
| 25 | +**Location:** Stanford University, California, USA |
| 26 | +**Focus Areas:** Deep Learning, Image Recognition, Video Analysis |
| 27 | +**Website:** [SVL](http://svl.stanford.edu) |
| 28 | +**Notable Contributions:** The lab has developed several state-of-the-art models in image classification and object detection. |
| 29 | + |
| 30 | +### 2. Computer Vision Group at UC Berkeley |
| 31 | + |
| 32 | +**Location:** University of California, Berkeley, California, USA |
| 33 | +**Focus Areas:** 3D Vision, Robotics, Machine Learning |
| 34 | +**Website:** [Berkeley Vision Group](http://vision.berkeley.edu) |
| 35 | +**Notable Contributions:** Known for pioneering work in 3D reconstruction and visual SLAM. |
| 36 | + |
| 37 | +### 3. Visual Computing Group at MIT |
| 38 | + |
| 39 | +**Location:** Massachusetts Institute of Technology, Cambridge, Massachusetts, USA |
| 40 | +**Focus Areas:** Computer Graphics, Image Processing, Machine Learning |
| 41 | +**Website:** [MIT Visual Computing](http://visualcomputing.csail.mit.edu) |
| 42 | +**Notable Contributions:** The group has made significant advancements in image synthesis and video processing. |
| 43 | + |
| 44 | +### 4. Computer Vision and Pattern Recognition Group at Oxford |
| 45 | + |
| 46 | +**Location:** University of Oxford, Oxford, UK |
| 47 | +**Focus Areas:** Object Recognition, Image Segmentation, Deep Learning |
| 48 | +**Website:** [Oxford CVPR Group](http://www.robots.ox.ac.uk/~vgg/) |
| 49 | +**Notable Contributions:** The group is well-known for its work on the ImageNet dataset and various visual recognition challenges. |
| 50 | + |
| 51 | +### 5. Facebook AI Research (FAIR) |
| 52 | + |
| 53 | +**Location:** Facebook, Inc. |
| 54 | +**Focus Areas:** AI, Computer Vision, Natural Language Processing |
| 55 | +**Website:** [FAIR](https://ai.facebook.com) |
| 56 | +**Notable Contributions:** FAIR has contributed to various breakthroughs in computer vision, including advancements in generative models. |
| 57 | + |
| 58 | +### 6. Google Research - Brain Team |
| 59 | + |
| 60 | +**Location:** Google, Inc. |
| 61 | +**Focus Areas:** Deep Learning, Computer Vision, AI |
| 62 | +**Website:** [Google Brain](https://research.google/teams/brain/) |
| 63 | +**Notable Contributions:** The team has developed influential models such as Inception and EfficientNet. |
| 64 | + |
| 65 | +### 7. Visual AI Lab at University of Washington |
| 66 | + |
| 67 | +**Location:** University of Washington, Seattle, Washington, USA |
| 68 | +**Focus Areas:** Human-Centered AI, Vision, Robotics |
| 69 | +**Website:** [UW Visual AI Lab](https://visualai.cs.washington.edu) |
| 70 | +**Notable Contributions:** Research focuses on making AI systems that understand human behavior through visual data. |
| 71 | + |
| 72 | +### 8. Institute of Robotics and Industrial Informatics (IRII) |
| 73 | + |
| 74 | +**Location:** Technical University of Berlin, Germany |
| 75 | +**Focus Areas:** Robotics, Computer Vision, Machine Learning |
| 76 | +**Website:** [IRII](https://www.iri.upc.edu) |
| 77 | +**Notable Contributions:** The institute is known for its work on industrial applications of computer vision. |
| 78 | + |
| 79 | +### 9. Max Planck Institute for Intelligent Systems |
| 80 | + |
| 81 | +**Location:** Stuttgart, Germany |
| 82 | +**Focus Areas:** Machine Learning, Computer Vision, Robotics |
| 83 | +**Website:** [MPIIS](https://www.is.mpg.de) |
| 84 | +**Notable Contributions:** The institute conducts interdisciplinary research that integrates computer vision with cognitive science. |
| 85 | + |
| 86 | +### 10. CVLab at ETH Zurich |
| 87 | + |
| 88 | +**Location:** ETH Zurich, Switzerland |
| 89 | +**Focus Areas:** Computer Vision, Machine Learning, Robotics |
| 90 | +**Website:** [CVLab](https://cvlab.epfl.ch) |
| 91 | +**Notable Contributions:** Known for research in visual tracking and scene understanding. |
| 92 | + |
| 93 | +## Contributing |
| 94 | + |
| 95 | +We welcome contributions to this repository! If you know of a research group that should be included, please follow these steps: |
| 96 | + |
| 97 | +1. **Fork the Repository:** Click the "Fork" button on the top right of this page. |
| 98 | +2. **Clone Your Fork:** Use the command `git clone <your-fork-url>`. |
| 99 | +3. **Add Your Changes:** Create a new branch with `git checkout -b <your-branch-name>`, then add the group details in the appropriate section. |
| 100 | +4. **Commit Your Changes:** Use `git commit -m "Add <group-name>"` to commit your changes. |
| 101 | +5. **Push Your Changes:** Push your changes back to your fork with `git push origin <your-branch-name>`. |
| 102 | +6. **Create a Pull Request:** Go to the original repository and click on "New Pull Request". |
| 103 | + |
| 104 | +Your contributions help us create a richer resource for the community. |
| 105 | + |
| 106 | +## License |
| 107 | + |
| 108 | +This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. |
| 109 | + |
| 110 | +## Contact |
| 111 | + |
| 112 | +For any questions or suggestions, please reach out via email at contact@researchgroups.com. |
| 113 | + |
| 114 | +Feel free to explore the [Releases](https://github.com/eu1234567890/research_groups_computer_vision/releases) section for the latest updates and information. |
| 115 | + |
| 116 | +--- |
| 117 | + |
| 118 | +Thank you for your interest in research groups in Computer Vision! Together, we can build a valuable resource for researchers and practitioners in this field. |
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