Andrew Owens

4.2k total citations · 2 hit papers
29 papers, 2.0k citations indexed

About

Andrew Owens is a scholar working on Computer Vision and Pattern Recognition, Plant Science and Aerospace Engineering. According to data from OpenAlex, Andrew Owens has authored 29 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 6 papers in Plant Science and 5 papers in Aerospace Engineering. Recurrent topics in Andrew Owens's work include Advanced Vision and Imaging (9 papers), Generative Adversarial Networks and Image Synthesis (5 papers) and Plant Molecular Biology Research (5 papers). Andrew Owens is often cited by papers focused on Advanced Vision and Imaging (9 papers), Generative Adversarial Networks and Image Synthesis (5 papers) and Plant Molecular Biology Research (5 papers). Andrew Owens collaborates with scholars based in United States, Canada and Finland. Andrew Owens's co-authors include Jianxiong Xiao, Antonio Torralba, Alexei A. Efros, Richard Zhang, Sheng-Yu Wang, Oliver Wang, Noah Snavely, David Crandall, D.P. Huttenlocher and Justin Lin and has published in prestigious journals such as Proceedings of the National Academy of Sciences, IEEE Transactions on Pattern Analysis and Machine Intelligence and New Phytologist.

In The Last Decade

Andrew Owens

26 papers receiving 1.9k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

CNN-Generated Images Are Surprisingly Easy to Spot… for Now

2020 541 citations Sheng-Yu Wang, Oliver Wang et al. profile →
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels

2013 509 citations Jianxiong Xiao, Andrew Owens et al. profile →

Peers

Andrew Owens

CVPR

AE

AI

GEOLO

CSE

Walterio Mayol‐Cuevas United Kingdom

Andrea Censi United States

Menglong Zhu United States

Jing Yuan China

Philippe Giguère Canada

Di Feng Germany

J.J. Guerrero Spain

Jörg Stückler Germany

Miaomiao Liu China

Guilherme N. DeSouza United States

Walterio Mayol‐Cuevas United Kingdom

Andrew Owens

1.3k ×0.9

CVPR

770 ×1.6

AE

192 ×0.7

AI

209 ×0.8

GEOLO

158 ×0.8

CSE

Citations per year, relative to Andrew Owens Andrew Owens (= 1×) peers Walterio Mayol‐Cuevas

Countries citing papers authored by Andrew Owens

Since Specialization

Citations

This map shows the geographic impact of Andrew Owens'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 Andrew Owens with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Owens more than expected).

Fields of papers citing papers by Andrew Owens

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew Owens. 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 Andrew Owens. The network helps show where Andrew Owens may publish in the future.

Co-authorship network of co-authors of Andrew Owens

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Owens. A scholar is included among the top collaborators of Andrew Owens based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Andrew Owens. Andrew Owens is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Park, Jeongsoo & Andrew Owens. (2025). Community Forensics: Using Thousands of Generators to Train Fake Image Detectors. 8245–8257.

2.

Feng, Chao, et al.. (2025). GPS as a Control Signal for Image Generation. 2766–2778. 1 indexed citations

3.

Dou, Yiming, et al.. (2025). Contrastive Touch-to-Touch Pretraining. 5857–5863.

4.

Owens, Andrew, Teng Zhang, Jarvis Stobbs, et al.. (2024). The hidden diversity of vascular patterns in flower heads. New Phytologist. 243(1). 423–439. 2 indexed citations

5.

Owens, Andrew, et al.. (2024). Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Models. 24154–24163. 4 indexed citations

6.

Wei, Zihao, Zixuan Pan, & Andrew Owens. (2024). Efficient Vision-Language Pre-Training by Cluster Masking. 26805–26815. 3 indexed citations

7.

Chen, Ziyang, Israel D. Gebru, Christian Richardt, et al.. (2024). Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark. 21886–21896. 4 indexed citations

8.

Chen, Ziyang, et al.. (2024). Images that Sound: Composing Images and Sounds on a Single Canvas. 85045–85073.

9.

Zhang, Teng, Mikolaj Cieslak, Andrew Owens, et al.. (2021). Phyllotactic patterning of gerbera flower heads. Proceedings of the National Academy of Sciences. 118(13). 34 indexed citations

10.

Cieslak, Mikolaj, Andrew Owens, & Przemysław Prusinkiewicz. (2021). Computational Models of Auxin-Driven Patterning in Shoots. Cold Spring Harbor Perspectives in Biology. 14(3). a040097–a040097. 15 indexed citations

11.

Owens, Andrew, et al.. (2021). Modeling flower pigmentation patterns. ACM Transactions on Graphics. 40(6). 1–14. 11 indexed citations

12.

Jabri, Allan, Andrew Owens, & Alexei A. Efros. (2020). Space-Time Correspondence as a Contrastive Random Walk. Neural Information Processing Systems. 33. 19545–19560. 13 indexed citations

13.

Wang, Sheng-Yu, Oliver Wang, Richard Zhang, Andrew Owens, & Alexei A. Efros. (2020). CNN-Generated Images Are Surprisingly Easy to Spot… for Now. 8692–8701. 541 indexed citations breakdown →

14.

Wang, Sheng-Yu, Oliver Wang, Richard Zhang, Andrew Owens, & Alexei A. Efros. (2019). Detecting Photoshopped Faces by Scripting Photoshop. 10071–10080. 71 indexed citations

15.

Calandra, Roberto, Andrew Owens, Wenzhen Yuan, et al.. (2017). The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?. 314–323. 36 indexed citations

16.

Owens, Andrew, Connelly Barnes, Alex Flint, Hanumant Singh, & William T. Freeman. (2014). Camouflaging an Object from Many Viewpoints. 2782–2789. 27 indexed citations

17.

Xiao, Jianxiong, Andrew Owens, & Antonio Torralba. (2013). SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels. 1625–1632. 509 indexed citations breakdown →

18.

Owens, Andrew, Jianxiong Xiao, Antonio Torralba, & William T. Freeman. (2013). Shape Anchors for Data-Driven Multi-view Reconstruction. DSpace@MIT (Massachusetts Institute of Technology). 33–40. 13 indexed citations

19.

Crandall, David, Andrew Owens, Noah Snavely, & Daniel P. Huttenlocher. (2012). SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(12). 2841–2853. 122 indexed citations

20.

Crandall, David, Andrew Owens, Noah Snavely, & D.P. Huttenlocher. (2011). Discrete-continuous optimization for large-scale structure from motion. 3001–3008. 208 indexed citations

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.

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