Edward Johns

3.4k total citations · 1 hit paper
35 papers, 1.5k citations indexed

About

Edward Johns is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Edward Johns has authored 35 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Computer Vision and Pattern Recognition, 13 papers in Artificial Intelligence and 13 papers in Aerospace Engineering. Recurrent topics in Edward Johns's work include Advanced Image and Video Retrieval Techniques (12 papers), Robotics and Sensor-Based Localization (12 papers) and Robot Manipulation and Learning (10 papers). Edward Johns is often cited by papers focused on Advanced Image and Video Retrieval Techniques (12 papers), Robotics and Sensor-Based Localization (12 papers) and Robot Manipulation and Learning (10 papers). Edward Johns collaborates with scholars based in United Kingdom, China and Switzerland. Edward Johns's co-authors include Andrew J. Davison, Shikun Liu, Guang‐Zhong Yang, Stefan Leutenegger, Norman Di Palo, Guillermo Garcia-Hernando, Tae‐Kyun Kim, Gary Frost, Benny Lo and Jindong Liu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and IEEE Robotics and Automation Letters.

In The Last Decade

Edward Johns

34 papers receiving 1.4k citations

Hit Papers

End-To-End Multi-Task Learning With Attention 2019 2026 2021 2023 2019 200 400 600
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.

  1. End-To-End Multi-Task Learning With Attention
    2019 687 citations Shikun Liu, Edward Johns et al. Spiral (Imperial College London) profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Edward Johns United Kingdom 17 770 485 283 262 195 35 1.5k
Zsolt Kira United States 22 1.2k 1.6× 765 1.6× 158 0.6× 245 0.9× 104 0.5× 68 1.9k
Jing Pan China 20 914 1.2× 360 0.7× 119 0.4× 201 0.8× 114 0.6× 93 1.7k
Di Feng Germany 10 738 1.0× 325 0.7× 131 0.5× 267 1.0× 125 0.6× 10 1.3k
Yuichi Motai United States 20 402 0.5× 278 0.6× 178 0.6× 197 0.8× 122 0.6× 70 1.1k
Xavier Alameda-Pineda France 24 895 1.2× 582 1.2× 101 0.4× 130 0.5× 302 1.5× 69 2.0k
Sridhar Lakshmanan United States 16 1.4k 1.8× 306 0.6× 177 0.6× 175 0.7× 128 0.7× 68 2.0k
Yuexin Ma China 24 1.2k 1.6× 290 0.6× 163 0.6× 377 1.4× 125 0.6× 76 2.0k
Zulfiqar Habib Pakistan 24 1.1k 1.4× 344 0.7× 226 0.8× 282 1.1× 152 0.8× 85 1.6k
N. M. Fonseca Ferreira Portugal 17 404 0.5× 359 0.7× 253 0.9× 134 0.5× 157 0.8× 105 1.4k
Du Q. Huynh Australia 22 1.3k 1.7× 482 1.0× 175 0.6× 281 1.1× 271 1.4× 79 2.0k

Countries citing papers authored by Edward Johns

Since Specialization
Citations

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

Fields of papers citing papers by Edward Johns

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Edward Johns

This figure shows the co-authorship network connecting the top 25 collaborators of Edward Johns. A scholar is included among the top collaborators of Edward Johns 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 Edward Johns. Edward Johns is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Palo, Norman Di, et al.. (2025). R+X: Retrieval and Execution from Everyday Human Videos. 8284–8290.
2.
Ren, Yifei, et al.. (2024). Dream2Real: Zero-Shot 3D Object Rearrangement with Vision-Language Models. 4796–4803. 8 indexed citations
3.
Palo, Norman Di, et al.. (2024). Language Models as Zero-Shot Trajectory Generators. IEEE Robotics and Automation Letters. 9(7). 6728–6735. 21 indexed citations
4.
Palo, Norman Di & Edward Johns. (2024). DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models. 2798–2805. 7 indexed citations
5.
Johns, Edward, et al.. (2024). Adapting Skills to Novel Grasps: A Self-Supervised Approach. 10897–10904. 1 indexed citations
6.
Palo, Norman Di & Edward Johns. (2024). On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation. IEEE Robotics and Automation Letters. 9(3). 2032–2039. 2 indexed citations
7.
Johns, Edward, et al.. (2023). DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics. IEEE Robotics and Automation Letters. 8(7). 3956–3963. 45 indexed citations
8.
Palo, Norman Di, et al.. (2022). Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 8614–8621. 20 indexed citations
9.
Xu, Hui, et al.. (2021). DROID: Minimizing the Reality Gap Using Single-Shot Human Demonstration. IEEE Robotics and Automation Letters. 6(2). 3168–3175. 19 indexed citations
10.
Johns, Edward, et al.. (2021). PERIL: Probabilistic Embeddings for hybrid Meta-Reinforcement and Imitation Learning. 1 indexed citations
11.
Johns, Edward, et al.. (2021). Hybrid ICP. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 6801–6808. 3 indexed citations
12.
Du, Liang, Xiaoqing Ye, Xiao Tan, et al.. (2021). AGO-Net: Association-Guided 3D Point Cloud Object Detection Network. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(11). 1–1. 34 indexed citations
13.
Ding, Zihan, et al.. (2020). Crossing the gap: a deep dive into zero-shot sim-to-real transfer for dynamics. Spiral (Imperial College London). 19 indexed citations
14.
Xiao, Bo, et al.. (2020). Constrained-Space Optimization and Reinforcement Learning for Complex Tasks. IEEE Robotics and Automation Letters. 5(2). 683–690. 10 indexed citations
15.
Garcia-Hernando, Guillermo, Edward Johns, & Tae‐Kyun Kim. (2020). Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning. 9561–9568. 41 indexed citations
16.
Liu, Shikun, Andrew J. Davison, & Edward Johns. (2019). Self-Supervised Generalisation with Meta Auxiliary Learning. Spiral (Imperial College London). 32. 1677–1687. 8 indexed citations
17.
Ye, Menglong, Edward Johns, Benjamin Walter, Alexander Meining, & Guang‐Zhong Yang. (2017). An image retrieval framework for real-time endoscopic image retargeting. International Journal of Computer Assisted Radiology and Surgery. 12(8). 1281–1292. 10 indexed citations
18.
Johns, Edward, et al.. (2017). Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery. 27–28. 69 indexed citations
19.
Johns, Edward & Guang‐Zhong Yang. (2013). Feature Co-occurrence Maps: Appearance-based localisation throughout the day. 17. 3212–3218. 68 indexed citations
20.
Johns, Edward & Guang‐Zhong Yang. (2010). Scene association for mobile robot navigation. 4698–4703. 2 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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