Nikos Komodakis

22.7k total citations · 1 hit paper
68 papers, 3.7k citations indexed

About

Nikos Komodakis is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Nikos Komodakis has authored 68 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Computer Vision and Pattern Recognition, 19 papers in Artificial Intelligence and 9 papers in Media Technology. Recurrent topics in Nikos Komodakis's work include Advanced Image and Video Retrieval Techniques (25 papers), Medical Image Segmentation Techniques (18 papers) and Advanced Vision and Imaging (13 papers). Nikos Komodakis is often cited by papers focused on Advanced Image and Video Retrieval Techniques (25 papers), Medical Image Segmentation Techniques (18 papers) and Advanced Vision and Imaging (13 papers). Nikos Komodakis collaborates with scholars based in France, Greece and Germany. Nikos Komodakis's co-authors include Georgios Tziritas, Nikos Paragios, Spyros Gidaris, Jean‐Christophe Pesquet, Ben Glocker, Praveer Singh, Nassir Navab, Wenbin Zou, Chaohui Wang and Aristeidis Sotiras and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Nikos Komodakis

64 papers receiving 3.5k citations

Hit Papers

Dynamic Few-Shot Visual Learning Without Forgetting 2018 2026 2020 2023 2018 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nikos Komodakis France 26 2.6k 1.0k 546 433 352 68 3.7k
Laurent Najman France 25 2.8k 1.1× 693 0.7× 664 1.2× 382 0.9× 162 0.5× 95 4.2k
Stella X. Yu United States 31 2.8k 1.1× 2.0k 1.9× 379 0.7× 363 0.8× 176 0.5× 123 4.5k
Christoph Schnörr Germany 29 2.7k 1.0× 505 0.5× 312 0.6× 374 0.9× 634 1.8× 128 3.7k
Yu-Chiang Frank Wang Taiwan 35 2.8k 1.1× 1.3k 1.3× 733 1.3× 218 0.5× 355 1.0× 153 3.9k
Fan Zhu China 29 2.5k 0.9× 1.5k 1.4× 250 0.5× 260 0.6× 382 1.1× 76 3.7k
Ishan Misra United States 18 3.0k 1.2× 2.2k 2.1× 266 0.5× 340 0.8× 248 0.7× 35 4.7k
Jonathan Masci Switzerland 15 1.9k 0.7× 1.2k 1.2× 372 0.7× 185 0.4× 549 1.6× 26 3.8k
Samuel Rota Bulò Italy 29 2.1k 0.8× 1.3k 1.3× 234 0.4× 192 0.4× 255 0.7× 79 3.3k
Hugo Touvron France 5 2.4k 0.9× 1.7k 1.7× 407 0.7× 445 1.0× 130 0.4× 7 4.1k
Leo Grady United States 29 2.8k 1.1× 715 0.7× 415 0.8× 919 2.1× 308 0.9× 75 4.4k

Countries citing papers authored by Nikos Komodakis

Since Specialization
Citations

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

Fields of papers citing papers by Nikos Komodakis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nikos Komodakis

This figure shows the co-authorship network connecting the top 25 collaborators of Nikos Komodakis. A scholar is included among the top collaborators of Nikos Komodakis 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 Nikos Komodakis. Nikos Komodakis 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.
Jiang, Xiaotong, Olivier Teboul, Nikos Komodakis, et al.. (2025). Deep learning detection of acute and sub-acute lesion activity from single-timepoint conventional brain MRI in multiple sclerosis. Medical Image Analysis. 105. 103619–103619.
2.
3.
Kakogeorgiou, Ioannis, Spyros Gidaris, Κωνσταντίνος Καράντζαλος, & Nikos Komodakis. (2024). SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers. 22776–22786. 1 indexed citations
5.
Gidaris, Spyros, Andrei Bursuc, Gilles Puy, et al.. (2021). OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning. 6826–6836. 44 indexed citations
6.
Oyallon, Edouard, Sergey Zagoruyko, Nikos Komodakis, et al.. (2018). Scattering Networks for Hybrid Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 41(9). 2208–2221. 40 indexed citations
7.
Simonovsky, Martin & Nikos Komodakis. (2016). OnionNet: Sharing Features in Cascaded Deep Classifiers. arXiv (Cornell University). 1 indexed citations
8.
Komodakis, Nikos, Manish Kumar, & Nikos Paragios. (2016). (Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision. SPIRE - Sciences Po Institutional REpository. 10(1). 1–102. 2 indexed citations
9.
Gidaris, Spyros & Nikos Komodakis. (2016). LocNet: Improving Localization Accuracy for Object Detection. arXiv (Cornell University). 789–798. 1 indexed citations
10.
Vakalopoulou, Maria, Κωνσταντίνος Καράντζαλος, Nikos Komodakis, & Nikos Paragios. (2016). Graph-Based Registration, Change Detection, and Classification in Very High Resolution Multitemporal Remote Sensing Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(7). 2940–2951. 22 indexed citations
11.
Komodakis, Nikos, Nikos Paragios, & Georgios Tziritas. (2013). MRF energy minimization and beyond via dual decomposition. SPIRE - Sciences Po Institutional REpository. 8 indexed citations
12.
Zikic, Darko, Ben Glocker, Oliver Kutter, et al.. (2010). Linear intensity-based image registration by Markov random fields and discrete optimization. Medical Image Analysis. 14(4). 550–562. 31 indexed citations
13.
Neji, Radhouène, et al.. (2009). Clustering of the Human Skeletal Muscle Fibers Using Linear Programming and Angular Hilbertian Metrics. Lecture notes in computer science. 21. 14–25. 3 indexed citations
14.
Sotiras, Aristeidis, Nikos Komodakis, Ben Glocker, Jean‐François Deux, & Nikos Paragios. (2009). Graphical Models and Deformable Diffeomorphic Population Registration Using Global and Local Metrics. Lecture notes in computer science. 12(Pt 1). 672–679. 12 indexed citations
15.
Komodakis, Nikos, Nikos Paragios, & Georgios Tziritas. (2008). Clustering via LP-based Stabilities. Neural Information Processing Systems. 21. 865–872. 21 indexed citations
16.
Wachinger, Christian, et al.. (2008). Deformable Mosaicing for Whole-Body MRI. Lecture notes in computer science. 11(Pt 2). 113–121. 14 indexed citations
17.
Komodakis, Nikos & Georgios Tziritas. (2007). Approximate Labeling via Graph Cuts Based on Linear Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(8). 1436–1453. 139 indexed citations
18.
Glocker, Ben, et al.. (2007). Inter and Intra-modal Deformable Registration: Continuous Deformations Meet Efficient Optimal Linear Programming. Lecture notes in computer science. 20. 408–420. 37 indexed citations
19.
Glocker, Ben, Nikos Komodakis, Nikos Paragios, et al.. (2007). Primal/Dual Linear Programming and Statistical Atlases for Cartilage Segmentation. Lecture notes in computer science. 10(Pt 2). 536–543. 25 indexed citations
20.
Komodakis, Nikos & Georgios Tziritas. (2005). Approximate Labeling via the Primal-Dual Schema. 4 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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