Rob Fergus
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Advanced Image Processing Techniques
- Advanced Vision and Imaging
- Multimodal Machine Learning Applications
- Artificial Intelligence top 0.1%
- Domain Adaptation and Few-Shot Learning
- Explainable Artificial Intelligence (XAI)
Papers in
-
- Advanced Image and Video Retrieval Techniques 12
- Advanced Neural Network Applications 7
- Human Pose and Action Recognition 3
- Image Enhancement Techniques 3
- Multimodal Machine Learning Applications 2
- Advanced Image Processing Techniques 2
-
- Reinforcement Learning in Robotics 6
- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- Matthew D. Zeiler (5 shared papers)Pietro Perona (10 shared papers)Li Fei-Fei (6 shared papers)Manohar Paluri (6 shared papers)Lubomir D. Bourdev (6 shared papers)Du Tran (3 shared papers)Lorenzo Torresani (3 shared papers)Antonio B. Torralba (8 shared papers)
- Journals
- ACM Transactions on Graphics (4 papers)IEEE Transactions on Pattern Analysis and Machine Intelligence (3 papers)The Astrophysical Journal (2 papers)International Journal of Computer Vision (1 paper)Proceedings of the National Academy of Sciences (1 paper)
- Partner nations
- United StatesUnited KingdomIsrael
In The Last Decade
Rob Fergus
66 papers receiving 47.6k citations
Rob Fergus's Hit Papers
Peers
Comparison fields: 5 of 166
- Computer Vision and Pattern Recognition 23.3k
- Artificial Intelligence 6.0k
- Media Technology 822
- Human-Computer Interaction 531
- Signal Processing 473
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 67 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Visualizing and Understanding Convolutional Networks Hit paper breakdown → | 2014 | 11245 |
| 2 | Learning Spatiotemporal Features with 3D Convolutional Networks Hit paper breakdown → | 2015 | 7251 |
| 3 | Indoor Segmentation and Support Inference from RGBD Images Hit paper breakdown → | 2012 | 4200 |
| 4 | One-shot learning of object categories Hit paper breakdown → | 2006 | 2230 |
| 5 | Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture Hit paper breakdown → | 2015 | 2124 |
| 6 | Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences Hit paper breakdown → | 2021 | 1843 |
| 7 | Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories Hit paper breakdown → | 2007 | 1713 |
| 8 | Spectral Hashing Hit paper breakdown → | 2008 | 1617 |
| 9 | Object class recognition by unsupervised scale-invariant learning Hit paper breakdown → | 2003 | 1596 |
| 10 | 80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition Hit paper breakdown → | 2008 | 1477 |
| 11 | Removing camera shake from a single photograph Hit paper breakdown → | 2006 | 1428 |
| 12 | Deconvolutional networks Hit paper breakdown → | 2010 | 1233 |
| 13 | Regularization of Neural Networks using DropConnect Hit paper breakdown → | 2013 | 1184 |
| 14 | Adaptive deconvolutional networks for mid and high level feature learning Hit paper breakdown → | 2011 | 934 |
| 15 | Fast Image Deconvolution using Hyper-Laplacian Priors Hit paper breakdown → | 2009 | 903 |
| 16 | Blind deconvolution using a normalized sparsity measure Hit paper breakdown → | 2011 | 862 |
| 17 | Image and depth from a conventional camera with a coded aperture Hit paper breakdown → | 2007 | 857 |
| 18 | Image and depth from a conventional camera with a coded aperture Hit paper breakdown → | 2007 | 846 |
| 19 | Small codes and large image databases for recognition Hit paper breakdown → | 2008 | 587 |
| 20 | Learning object categories from Google's image search Hit paper breakdown → | 2005 | 570 |
About Rob Fergus
Rob Fergus is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Astronomy and Astrophysics and Experimental and Cognitive Psychology, having authored 67 papers that have together received 49.1k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (12 papers), Advanced Neural Network Applications (7 papers), Reinforcement Learning in Robotics (6 papers), Human Pose and Action Recognition (3 papers), Image Enhancement Techniques (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Multimodal Machine Learning Applications (2 papers) and Advanced Image Processing Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (23.3k citations), Artificial Intelligence (6.0k citations), Media Technology (822 citations), Human-Computer Interaction (531 citations) and Signal Processing (473 citations). Rob Fergus has collaborated with scholars based in United States, United Kingdom and Israel. Frequent co-authors include Matthew D. Zeiler, Pietro Perona, Li Fei-Fei, Manohar Paluri, Lubomir D. Bourdev, Du Tran, Lorenzo Torresani, Antonio B. Torralba, Nathan Silberman and Dilip Krishnan. Their work appears in journals such as ACM Transactions on Graphics, IEEE Transactions on Pattern Analysis and Machine Intelligence, The Astrophysical Journal, International Journal of Computer Vision and Proceedings of the National Academy of Sciences.
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.