Samuel Schulter

1.9k total citations · 1 hit paper
31 papers, 854 citations indexed

About

Samuel Schulter is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Geology. According to data from OpenAlex, Samuel Schulter has authored 31 papers receiving a total of 854 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Computer Vision and Pattern Recognition, 14 papers in Artificial Intelligence and 5 papers in Geology. Recurrent topics in Samuel Schulter's work include Multimodal Machine Learning Applications (8 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Neural Network Applications (7 papers). Samuel Schulter is often cited by papers focused on Multimodal Machine Learning Applications (8 papers), Domain Adaptation and Few-Shot Learning (8 papers) and Advanced Neural Network Applications (7 papers). Samuel Schulter collaborates with scholars based in United States, Austria and Germany. Samuel Schulter's co-authors include Horst Bischof, Christian Leistner, Peter M. Roth, Manmohan Chandraker, Paul Wohlhart, Buyu Liu, Matthias Rüther, Gernot Riegler, Amir Saffari and Bingbing Zhuang and has published in prestigious journals such as Journal on Computing and Cultural Heritage, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and Lirias (KU Leuven).

In The Last Decade

Samuel Schulter

27 papers receiving 834 citations

Hit Papers

Fast and accurate image upscaling with super-resolution f... 2015 2026 2018 2022 2015 100 200 300 400
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. Fast and accurate image upscaling with super-resolution forests
    2015 429 citations Samuel Schulter, Christian Leistner et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Samuel Schulter United States 14 756 330 121 52 43 31 854
David Ferstl Austria 7 648 0.9× 339 1.0× 116 1.0× 59 1.1× 14 0.3× 14 714
Seoung Wug Oh United States 13 982 1.3× 246 0.7× 82 0.7× 48 0.9× 14 0.3× 22 1.1k
Xingjia Pan China 13 707 0.9× 198 0.6× 243 2.0× 149 2.9× 16 0.4× 17 888
Pan Ji China 15 783 1.0× 218 0.7× 230 1.9× 124 2.4× 33 0.8× 37 915
Faouzi Ghorbel Tunisia 14 558 0.7× 79 0.2× 77 0.6× 49 0.9× 21 0.5× 99 667
Donggeun Yoo South Korea 8 592 0.8× 136 0.4× 142 1.2× 34 0.7× 8 0.2× 14 728
Shangchen Zhou Singapore 15 1.1k 1.4× 257 0.8× 100 0.8× 31 0.6× 10 0.2× 36 1.2k
Ruizhi Qiao China 8 678 0.9× 262 0.8× 115 1.0× 18 0.3× 53 1.2× 18 843
Ranjith Unnikrishnan United States 6 558 0.7× 199 0.6× 108 0.9× 116 2.2× 29 0.7× 9 726
Donghyeon Cho South Korea 14 720 1.0× 117 0.4× 328 2.7× 48 0.9× 14 0.3× 33 869

Countries citing papers authored by Samuel Schulter

Since Specialization
Citations

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

Fields of papers citing papers by Samuel Schulter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samuel Schulter

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel Schulter. A scholar is included among the top collaborators of Samuel Schulter 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 Samuel Schulter. Samuel Schulter 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.
2.
Schulter, Samuel, et al.. (2025). LiSu: A Dataset and Method for LiDAR Surface Normal Estimation. 17039–17049.
3.
Su, Jong-Chyi, et al.. (2024). AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving. 14695–14706. 6 indexed citations
4.
5.
Zhao, Shiyu, L. Zhao, Vijay Kumar B G, et al.. (2024). Generating Enhanced Negatives for Training Language-Based Object Detectors. 13592–13602. 3 indexed citations
6.
Zhao, Shiyu, Samuel Schulter, L. Zhao, et al.. (2024). Taming Self-Training for Open-Vocabulary Object Detection. 13938–13947. 5 indexed citations
7.
8.
Schulter, Samuel, et al.. (2023). Efficient Controllable Multi-Task Architectures. 5717–5728. 4 indexed citations
9.
Zhuang, Bingbing, et al.. (2023). NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization. 21404–21414. 17 indexed citations
10.
Simon, Christian, Masoud Faraki, Yi‐Hsuan Tsai, et al.. (2022). On Generalizing Beyond Domains in Cross-Domain Continual Learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9255–9264. 20 indexed citations
11.
Shin, Inkyu, Yi‐Hsuan Tsai, Bingbing Zhuang, et al.. (2022). MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16907–16916. 34 indexed citations
12.
Suh, Yumin, et al.. (2022). Controllable Dynamic Multi-Task Architectures. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10945–10954. 19 indexed citations
13.
Jiang, Ziyu, Buyu Liu, Samuel Schulter, Zhangyang Wang, & Manmohan Chandraker. (2020). Peek-a-Boo: Occlusion Reasoning in Indoor Scenes With Plane Representations. 110–118. 12 indexed citations
14.
Liu, Buyu, Bingbing Zhuang, Samuel Schulter, Pan Ji, & Manmohan Chandraker. (2020). Understanding Road Layout From Videos as a Whole. 13 indexed citations
15.
Wang, Ziyan, Buyu Liu, Samuel Schulter, & Manmohan Chandraker. (2019). A Parametric Top-View Representation of Complex Road Scenes. 10317–10325. 24 indexed citations
16.
Grabner, Alexander, et al.. (2017). Loss-Specific Training of Random Forests for Super-Resolution. TUGraz OPEN Library (Graz University of Technology). 1 indexed citations
17.
Zeppelzauer, Matthias, et al.. (2016). Interactive 3D Segmentation of Rock-Art by Enhanced Depth Maps and Gradient Preserving Regularization. Journal on Computing and Cultural Heritage. 9(4). 1–30. 34 indexed citations
18.
Schulter, Samuel, Paul Wohlhart, Christian Leistner, et al.. (2013). Alternating Decision Forests. 508–515. 42 indexed citations
19.
Schulter, Samuel, Christian Leistner, Paul Wohlhart, Peter M. Roth, & Horst Bischof. (2013). Alternating Regression Forests for Object Detection and Pose Estimation. 417–424. 31 indexed citations
20.
Wohlhart, Paul, Samuel Schulter, Martin Köstinger, Peter M. Roth, & Horst Bischof. (2012). Discriminative Hough Forests for Object Detection. 40.1–40.11. 10 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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