Maxime Devanne

1.1k total citations
23 papers, 484 citations indexed

About

Maxime Devanne is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Maxime Devanne has authored 23 papers receiving a total of 484 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 8 papers in Computer Vision and Pattern Recognition and 7 papers in Signal Processing. Recurrent topics in Maxime Devanne's work include Anomaly Detection Techniques and Applications (10 papers), Time Series Analysis and Forecasting (7 papers) and Music and Audio Processing (5 papers). Maxime Devanne is often cited by papers focused on Anomaly Detection Techniques and Applications (10 papers), Time Series Analysis and Forecasting (7 papers) and Music and Audio Processing (5 papers). Maxime Devanne collaborates with scholars based in France, Italy and Ethiopia. Maxime Devanne's co-authors include Stefano Berretti, Hazem Wannous, Pietro Pala, Mohamed Daoudi, Alberto Del Bimbo, Germain Forestier, Jonathan Weber, Valentin Dérangère, Cédric Wemmert and Caroline Truntzer and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Cybernetics and BioMed Research International.

In The Last Decade

Maxime Devanne

18 papers receiving 461 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maxime Devanne France 8 288 247 129 113 105 23 484
Lahcen Koutti Morocco 11 144 0.5× 113 0.5× 40 0.3× 45 0.4× 60 0.6× 60 381
Melih Kandemir Germany 12 302 1.0× 328 1.3× 14 0.1× 125 1.1× 35 0.3× 32 513
Alessandro Bruno Italy 12 391 1.4× 144 0.6× 28 0.2× 105 0.9× 23 0.2× 36 567
Hritam Basak India 8 191 0.7× 226 0.9× 53 0.4× 115 1.0× 12 0.1× 12 410
Dae Hoe Kim South Korea 10 319 1.1× 220 0.9× 30 0.2× 100 0.9× 60 0.6× 31 553
Xiaohan Nie China 9 479 1.7× 249 1.0× 247 1.9× 17 0.2× 120 1.1× 16 564
Mansoor Fateh Iran 12 153 0.5× 84 0.3× 44 0.3× 113 1.0× 17 0.2× 44 381
Marwa Elpeltagy Egypt 8 149 0.5× 109 0.4× 34 0.3× 48 0.4× 31 0.3× 9 317
Arnab Kumar Mishra India 9 97 0.3× 183 0.7× 64 0.5× 181 1.6× 81 0.8× 14 430
Sibei Yang China 15 511 1.8× 354 1.4× 33 0.3× 91 0.8× 10 0.1× 26 714

Countries citing papers authored by Maxime Devanne

Since Specialization
Citations

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

Fields of papers citing papers by Maxime Devanne

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxime Devanne

This figure shows the co-authorship network connecting the top 25 collaborators of Maxime Devanne. A scholar is included among the top collaborators of Maxime Devanne 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 Maxime Devanne. Maxime Devanne 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.
Devanne, Maxime, et al.. (2025). Establishing a unified evaluation framework for human motion generation: A comparative analysis of metrics. Computer Vision and Image Understanding. 254. 104337–104337.
2.
Devanne, Maxime, et al.. (2025). Look into the LITE in deep learning for time series classification. International Journal of Data Science and Analytics. 20(4). 4029–4049. 7 indexed citations
3.
Devanne, Maxime, et al.. (2024). Document Information Extraction: An Analysis of Invoice Anatomy. Applied Computational Intelligence and Soft Computing. 2024(1).
4.
Nguyen, Sao Mai, et al.. (2024). A Medical Low-Back Pain Physical Rehabilitation Database for Human Body Movement Analysis. SPIRE - Sciences Po Institutional REpository. 1–8. 2 indexed citations
5.
Devanne, Maxime, et al.. (2024). COCALITE: A Hybrid Model COmbining CAtch22 and LITE for Time Series Classification. SPIRE - Sciences Po Institutional REpository. 1229–1236.
6.
Fawaz, Hassan Ismail, Maxime Devanne, Jonathan Weber, et al.. (2023). Time series adversarial attacks: an investigation of smooth perturbations and defense approaches. International Journal of Data Science and Analytics. 19(1). 129–139. 2 indexed citations
7.
Devanne, Maxime, et al.. (2023). Estimating time series averages from latent space of multi-tasking neural networks. Knowledge and Information Systems. 65(11). 4967–5004. 1 indexed citations
8.
Devanne, Maxime, et al.. (2023). Correction: Estimating time series averages from latent space of multi-tasking neural networks. Knowledge and Information Systems. 66(2). 1521–1521. 1 indexed citations
9.
Devanne, Maxime, et al.. (2023). Evidential deep learning-based multi-modal environment perception for intelligent vehicles. SPIRE - Sciences Po Institutional REpository. 1 indexed citations
10.
Devanne, Maxime, et al.. (2023). Enhancing Time Series Classification with Self-Supervised Learning. SPIRE - Sciences Po Institutional REpository. 40–47. 5 indexed citations
11.
Devanne, Maxime, et al.. (2023). f-AnoGAN for non-destructive testing in industrial anomaly detection. 46–46. 3 indexed citations
12.
Devanne, Maxime, et al.. (2023). LITE: Light Inception with boosTing tEchniques for Time Series Classification. SPIRE - Sciences Po Institutional REpository. 1–10. 9 indexed citations
13.
Devanne, Maxime, Jonathan Weber, Caroline Truntzer, et al.. (2022). Weakly Supervised Learning using Attention gates for colon cancer histopathological image segmentation. Artificial Intelligence in Medicine. 133. 102407–102407. 13 indexed citations
14.
Nguyen, Sao Mai, et al.. (2022). Technical Feasibility of Supervision of Stretching Exercises by a Humanoid Robot Coach for Chronic Low Back Pain: The R‐COOL Randomized Trial. BioMed Research International. 2022(1). 5667223–5667223. 8 indexed citations
15.
Fondement, Frédéric, et al.. (2022). Semantics to the rescue of document‐based XML diff: A JATS case study. Software Practice and Experience. 52(6). 1496–1516. 2 indexed citations
16.
Devanne, Maxime, et al.. (2022). Real-time road detection implementation of UNet architecture for autonomous driving. SPIRE - Sciences Po Institutional REpository. 1–5. 4 indexed citations
17.
Devanne, Maxime, et al.. (2022). Deep Learning For Time Series Classification Using New Hand-Crafted Convolution Filters. 2022 IEEE International Conference on Big Data (Big Data). 972–981. 16 indexed citations
18.
Devanne, Maxime, et al.. (2022). A study of Knowledge Distillation in Fully Convolutional Network for Time Series Classification. 2022 International Joint Conference on Neural Networks (IJCNN). 1–8. 7 indexed citations
19.
Devanne, Maxime, et al.. (2022). Ensemble Clustering for Histopathological Images Segmentation using Convolutional Autoencoders. HAL (Le Centre pour la Communication Scientifique Directe). 933–940.
20.
Devanne, Maxime, Jonathan Weber, Caroline Truntzer, et al.. (2021). Deep learning for colon cancer histopathological images analysis. Computers in Biology and Medicine. 136. 104730–104730. 139 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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