Maxime Bucher

1.9k total citations
2 papers, 32 citations indexed

About

Maxime Bucher is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Maxime Bucher has authored 2 papers receiving a total of 32 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Computer Vision and Pattern Recognition, 1 paper in Computational Mechanics and 1 paper in Artificial Intelligence. Recurrent topics in Maxime Bucher's work include Human Pose and Action Recognition (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and 3D Shape Modeling and Analysis (1 paper). Maxime Bucher is often cited by papers focused on Human Pose and Action Recognition (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and 3D Shape Modeling and Analysis (1 paper). Maxime Bucher collaborates with scholars based in France. Maxime Bucher's co-authors include Gilles Puy, Renaud Marlet, Alexandre Boulch, Christophe Lallement and Christian Enz and has published in prestigious journals such as Journal of Global Health and arXiv (Cornell University).

In The Last Decade

Maxime Bucher

2 papers receiving 30 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maxime Bucher France	2	20	11	9	8	5	2	32
Shoumeng Qiu China	4	26 1.3×	4 0.4×	8 0.9×	9 1.1×	14 2.8×	11	41
Rahul Goel India	3	30 1.5×	16 1.5×	8 0.9×	8 1.0×	9 1.8×	6	56
Humam Alwassel Saudi Arabia	4	27 1.4×	26 2.4×	18 2.0×	13 1.6×	3 0.6×	4	52
Christine Allen-Blanchette United States	3	44 2.2×	32 2.9×	16 1.8×	5 0.6×	18 3.6×	5	73
Tiago Cortinhal Sweden	4	22 1.1×	12 1.1×	9 1.0×	2 0.3×	16 3.2×	5	39
Botao Ye China	4	20 1.0×	4 0.4×	4 0.4×	2 0.3×	14 2.8×	7	39
Qichen Fu United States	3	21 1.1×	4 0.4×	2 0.2×	4 0.5×	3 0.6×	4	28
Chu-Tak Li Hong Kong	3	35 1.8×	2 0.2×	3 0.3×	3 0.4×	8 1.6×	6	41
Adam R. Kosiorek United Kingdom	6	72 3.6×	10 0.9×	4 0.4×	24 3.0×	8 1.6×	7	88

Countries citing papers authored by Maxime Bucher

Since Specialization

Citations

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

Fields of papers citing papers by Maxime Bucher

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxime Bucher

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

All Works

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2 of 2 papers shown

1.

Boulch, Alexandre, et al.. (2021). Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds. arXiv (Cornell University). 992–1002. 29 indexed citations

2.

Lallement, Christophe, Maxime Bucher, & Christian Enz. (1995). The EKV MOST Model and the Associated Parameter Extraction. Journal of Global Health. 13. 6043–6043. 3 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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