Rob Fergus
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Image and Video Retrieval Techniques
- Advanced Image Processing Techniques
- Image Retrieval and Classification Techniques
- Advanced Vision and Imaging
- Image and Signal Denoising Methods
- Advanced Neural Network Applications
- Media Technology top 0.02%
- Image Processing Techniques and Applications
Papers in
-
- Advanced Image and Video Retrieval Techniques 21
- Image Retrieval and Classification Techniques 13
- Advanced Vision and Imaging 12
- Advanced Image Processing Techniques 7
- Human Pose and Action Recognition 6
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- Domain Adaptation and Few-Shot Learning 10
- Reinforcement Learning in Robotics 7
- Co-authors
- Pietro Perona (9 shared papers)Li Fei-Fei (5 shared papers)Antonio Torralba (6 shared papers)William T. Freeman (8 shared papers)Dilip Krishnan (7 shared papers)Matthew D. Zeiler (6 shared papers)David Eigen (4 shared papers)Yair Weiss (3 shared papers)
- Journals
- ACM Transactions on Graphics (4 papers)IEEE Transactions on Pattern Analysis and Machine Intelligence (3 papers)Journal of Vision (1 paper)International Journal of Computer Vision (1 paper)Proceedings of the IEEE (1 paper)
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Rob Fergus
62 papers receiving 21.8k citations
Rob Fergus's Hit Papers
Peers
Comparison fields: 5 of 198
- Computer Vision and Pattern Recognition 16.9k
- Media Technology 5.2k
- Artificial Intelligence 5.7k
- Signal Processing 823
- Aerospace Engineering 1.7k
Countries citing papers authored by Rob Fergus
This map shows the geographic impact of Rob Fergus's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rob Fergus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rob Fergus more than expected).
Fields of papers citing papers by Rob Fergus
This network shows the impact of papers produced by Rob Fergus. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rob Fergus. The network helps show where Rob Fergus may publish in the future.
Co-authors
The 25 scholars most cited alongside Rob Fergus, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 65 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | One-shot learning of object categories Hit paper breakdown → | 2006 | 1879 |
| 2 | Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture Hit paper breakdown → | 2015 | 1685 |
| 3 | Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences Hit paper breakdown → | 2021 | 1561 |
| 4 | Spectral Hashing Hit paper breakdown → | 2008 | 1441 |
| 5 | Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories Hit paper breakdown → | 2007 | 1428 |
| 6 | Object class recognition by unsupervised scale-invariant learning Hit paper breakdown → | 2003 | 1321 |
| 7 | 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition Hit paper breakdown → | 2008 | 1272 |
| 8 | Removing camera shake from a single photograph Hit paper breakdown → | 2006 | 1224 |
| 9 | Deconvolutional networks Hit paper breakdown → | 2010 | 1060 |
| 10 | Regularization of Neural Networks using DropConnect Hit paper breakdown → | 2013 | 960 |
| 11 | Adaptive deconvolutional networks for mid and high level feature learning Hit paper breakdown → | 2011 | 808 |
| 12 | Fast Image Deconvolution using Hyper-Laplacian Priors Hit paper breakdown → | 2009 | 806 |
| 13 | Image and depth from a conventional camera with a coded aperture Hit paper breakdown → | 2007 | 745 |
| 14 | Image and depth from a conventional camera with a coded aperture Hit paper breakdown → | 2007 | 737 |
| 15 | Blind deconvolution using a normalized sparsity measure Hit paper breakdown → | 2011 | 736 |
| 16 | Small codes and large image databases for recognition Hit paper breakdown → | 2008 | 510 |
| 17 | Learning object categories from Google's image search Hit paper breakdown → | 2005 | 495 |
| 18 | End-To-End Memory Networks Hit paper breakdown → | 2015 | 372 |
| 19 | Indoor scene segmentation using a structured light sensor Hit paper breakdown → | 2011 | 327 |
| 20 | Stochastic Pooling for Regularization of Deep Convolutional Neural Networks Hit paper breakdown → | 2013 | 326 |
About Rob Fergus
Rob Fergus is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Aerospace Engineering and Computational Mechanics, having authored 65 papers that have together received 22.7k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (21 papers), Image Retrieval and Classification Techniques (13 papers), Advanced Vision and Imaging (12 papers), Image Processing Techniques and Applications (10 papers), Domain Adaptation and Few-Shot Learning (10 papers), Advanced Image Processing Techniques (7 papers), Reinforcement Learning in Robotics (7 papers) and Human Pose and Action Recognition (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (16.9k citations), Media Technology (5.2k citations), Artificial Intelligence (5.7k citations), Signal Processing (823 citations) and Aerospace Engineering (1.7k citations). Rob Fergus has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Pietro Perona, Li Fei-Fei, Antonio Torralba, William T. Freeman, Dilip Krishnan, Matthew D. Zeiler, David Eigen, Yair Weiss, Andrew Zisserman and Graham W. Taylor. Their work appears in journals such as ACM Transactions on Graphics, IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Vision, International Journal of Computer Vision and Proceedings of the IEEE.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.