Wouter Van Gansbeke

1.3k total citations · 1 hit paper
6 papers, 718 citations indexed

About

Wouter Van Gansbeke is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Wouter Van Gansbeke has authored 6 papers receiving a total of 718 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 1 paper in Automotive Engineering. Recurrent topics in Wouter Van Gansbeke's work include Domain Adaptation and Few-Shot Learning (4 papers), Advanced Neural Network Applications (3 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Wouter Van Gansbeke is often cited by papers focused on Domain Adaptation and Few-Shot Learning (4 papers), Advanced Neural Network Applications (3 papers) and Advanced Image and Video Retrieval Techniques (1 paper). Wouter Van Gansbeke collaborates with scholars based in Belgium, Switzerland and Bulgaria. Wouter Van Gansbeke's co-authors include Luc Van Gool, Stamatios Georgoulis, Simon Vandenhende, Marc Proesmans, Dengxin Dai, Davy Neven and Bert De Brabandere and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and arXiv (Cornell University).

In The Last Decade

Wouter Van Gansbeke

5 papers receiving 705 citations

Hit Papers

Multi-Task Learning for Dense Prediction Tasks: A Survey 2021 2026 2022 2024 2021 100 200 300 400 500
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. Multi-Task Learning for Dense Prediction Tasks: A Survey
    2021 525 citations Simon Vandenhende, Stamatios Georgoulis et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Wouter Van Gansbeke Belgium 4 366 326 53 51 46 6 718
Simon Vandenhende Switzerland 5 382 1.0× 351 1.1× 51 1.0× 58 1.1× 48 1.0× 7 758
Stamatios Georgoulis Switzerland 8 503 1.4× 352 1.1× 84 1.6× 63 1.2× 62 1.3× 19 929
Qing Tian China 15 464 1.3× 298 0.9× 55 1.0× 33 0.6× 94 2.0× 97 767
Thangarajah Akilan Canada 17 437 1.2× 207 0.6× 88 1.7× 39 0.8× 60 1.3× 52 831
Renjie Song China 10 414 1.1× 381 1.2× 50 0.9× 31 0.6× 72 1.6× 29 824
Rafael Marcos Luque‐Baena Spain 15 338 0.9× 166 0.5× 80 1.5× 26 0.5× 61 1.3× 60 734
Azizi Abdullah Malaysia 15 284 0.8× 199 0.6× 31 0.6× 84 1.6× 71 1.5× 67 723
Yunbo Rao China 10 246 0.7× 226 0.7× 39 0.7× 31 0.6× 39 0.8× 50 622
Asra Aslam Ireland 6 489 1.3× 154 0.5× 54 1.0× 58 1.1× 77 1.7× 16 836
Wenhui Li China 17 540 1.5× 294 0.9× 53 1.0× 91 1.8× 91 2.0× 101 1.0k

Countries citing papers authored by Wouter Van Gansbeke

Since Specialization
Citations

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

Fields of papers citing papers by Wouter Van Gansbeke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wouter Van Gansbeke. 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 Wouter Van Gansbeke. The network helps show where Wouter Van Gansbeke may publish in the future.

Co-authorship network of co-authors of Wouter Van Gansbeke

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

All Works

6 of 6 papers shown
1.
2.
Gansbeke, Wouter Van, Simon Vandenhende, Stamatios Georgoulis, & Luc Van Gool. (2021). Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. arXiv (Cornell University). 10052–10062. 2 indexed citations
3.
Vandenhende, Simon, Stamatios Georgoulis, Wouter Van Gansbeke, et al.. (2021). Multi-Task Learning for Dense Prediction Tasks: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(7). 1–1. 525 indexed citations breakdown →
4.
Gansbeke, Wouter Van, Simon Vandenhende, Stamatios Georgoulis, & Luc Van Gool. (2021). Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 10032–10042. 161 indexed citations
5.
Gansbeke, Wouter Van, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, & Luc Van Gool. (2020). Learning To Classify Images Without Labels.. arXiv (Cornell University). 7 indexed citations
6.
Gansbeke, Wouter Van, Bert De Brabandere, Davy Neven, Marc Proesmans, & Luc Van Gool. (2019). End-to-end Lane Detection through Differentiable Least-Squares Fitting. arXiv (Cornell University). 905–913. 23 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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2026