Eli Shechtman
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Generative Adversarial Networks and Image Synthesis
- Human Pose and Action Recognition
- Advanced Image Processing Techniques
- Advanced Vision and Imaging
- Image Enhancement Techniques
- Advanced Image and Video Retrieval Techniques
-
- Computer Graphics and Visualization Techniques
Papers in
-
- Generative Adversarial Networks and Image Synthesis 98
- Face recognition and analysis 18
- Advanced Image and Video Retrieval Techniques 14
- Advanced Vision and Imaging 13
- Image Enhancement Techniques 8
- Visual Attention and Saliency Detection 7
-
- Computer Graphics and Visualization Techniques 23
- Co-authors
- Alexei A. Efros (10 shared papers)Richard Zhang (29 shared papers)Oliver Wang (10 shared papers)Phillip Isola (3 shared papers)Dan B Goldman (22 shared papers)Michal Irani (6 shared papers)Connelly Barnes (26 shared papers)M. Irani (6 shared papers)
- Journals
- ACM Transactions on Graphics (28 papers)Computer Graphics Forum (7 papers)IEEE Transactions on Pattern Analysis and Machine Intelligence (6 papers)Lecture notes in computer science (28 papers)Communications of the ACM (2 papers)
- Partner nations
- United StatesIsraelCzechia
In The Last Decade
Eli Shechtman
162 papers receiving 30.7k citations
Eli Shechtman's Hit Papers
Peers
Comparison fields: 5 of 150
- Computer Vision and Pattern Recognition 25.9k
- Computer Graphics and Computer-Aided Design 2.2k
- Computational Mechanics 3.2k
- Signal Processing 715
- Media Technology 605
Countries citing papers authored by Eli Shechtman
This map shows the geographic impact of Eli Shechtman'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 Eli Shechtman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eli Shechtman more than expected).
Fields of papers citing papers by Eli Shechtman
This network shows the impact of papers produced by Eli Shechtman. 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 Eli Shechtman. The network helps show where Eli Shechtman may publish in the future.
Co-authors
The 25 scholars most cited alongside Eli Shechtman, 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 166 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | The Unreasonable Effectiveness of Deep Features as a Perceptual Metric Hit paper breakdown → | 2018 | 9434 |
| 2 | PatchMatch Hit paper breakdown → | 2009 | 2271 |
| 3 | Actions as space-time shapes Hit paper breakdown → | 2005 | 1405 |
| 4 | Actions as Space-Time Shapes Hit paper breakdown → | 2007 | 1182 |
| 5 | In defense of Nearest-Neighbor based image classification Hit paper breakdown → | 2008 | 903 |
| 6 | Matching Local Self-Similarities across Images and Videos Hit paper breakdown → | 2007 | 900 |
| 7 | Generative Visual Manipulation on the Natural Image Manifold Hit paper breakdown → | 2016 | 833 |
| 8 | StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery Hit paper breakdown → | 2021 | 792 |
| 9 | Localizing Moments in Video with Natural Language Hit paper breakdown → | 2017 | 713 |
| 10 | High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis Hit paper breakdown → | 2017 | 672 |
| 11 | Space-Time Completion of Video Hit paper breakdown → | 2007 | 563 |
| 12 | Deep Photo Style Transfer Hit paper breakdown → | 2017 | 531 |
| 13 | Summarizing visual data using bidirectional similarity Hit paper breakdown → | 2008 | 508 |
| 14 | Multi-Concept Customization of Text-to-Image Diffusion Hit paper breakdown → | 2023 | 444 |
| 15 | The Generalized PatchMatch Correspondence Algorithm Hit paper breakdown → | 2010 | 443 |
| 16 | Image melding Hit paper breakdown → | 2012 | 440 |
| 17 | 2017 | 356 | |
| 18 | Robust patch-based hdr reconstruction of dynamic scenes Hit paper breakdown → | 2012 | 342 |
| 19 | State of the Art on Neural Rendering Hit paper breakdown → | 2020 | 321 |
| 20 | 2018 | 311 |
About Eli Shechtman
Eli Shechtman is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Computational Mechanics, Media Technology and Control and Systems Engineering, having authored 166 papers that have together received 31.5k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (98 papers), Computer Graphics and Visualization Techniques (23 papers), Face recognition and analysis (18 papers), Advanced Image and Video Retrieval Techniques (14 papers), Advanced Vision and Imaging (13 papers), 3D Shape Modeling and Analysis (10 papers), Image Enhancement Techniques (8 papers) and Visual Attention and Saliency Detection (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (25.9k citations), Computer Graphics and Computer-Aided Design (2.2k citations), Computational Mechanics (3.2k citations), Signal Processing (715 citations) and Media Technology (605 citations). Eli Shechtman has collaborated with scholars based in United States, Israel and Czechia. Frequent co-authors include Alexei A. Efros, Richard Zhang, Oliver Wang, Phillip Isola, Dan B Goldman, Michal Irani, Connelly Barnes, M. Irani, Adam Finkelstein and Lena Gorelick. Their work appears in journals such as ACM Transactions on Graphics, Computer Graphics Forum, IEEE Transactions on Pattern Analysis and Machine Intelligence, Lecture notes in computer science and Communications of the ACM.
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.