Christoph Käding

548 total citations
10 papers, 179 citations indexed

About

Christoph Käding is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Social Psychology. According to data from OpenAlex, Christoph Käding has authored 10 papers receiving a total of 179 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Social Psychology. Recurrent topics in Christoph Käding's work include Machine Learning and Algorithms (6 papers), Machine Learning and Data Classification (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Christoph Käding is often cited by papers focused on Machine Learning and Algorithms (6 papers), Machine Learning and Data Classification (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Christoph Käding collaborates with scholars based in Germany, United States and Republic of the Congo. Christoph Käding's co-authors include Joachim Denzler, Paul Bodesheim, Erik Rodner, Alexander Freytag, Tilo Burghardt, Milou Groenenberg, Hjalmar S. Kühl, Ralf Biedert, Andreas Dengel and Kim Rouven Liedtke and has published in prestigious journals such as SHILAP Revista de lepidopterología, Mammalian Biology and KI - Künstliche Intelligenz.

In The Last Decade

Christoph Käding

10 papers receiving 173 citations

Peers

Christoph Käding

CVPR

AI

ECOLO

SP

MB

Zhongqi Miao United States

Yujie Zhong China

Jiongxin Liu United States

Marcel Simon Germany

Dario Zanca Germany

Tom Arbuckle Ireland

Johann van der Merwe Australia

Elizabeth Bondi United States

Fabio Verdicchio Belgium

Zhongqi Miao United States

Christoph Käding

86 ×1.1

CVPR

84 ×1.1

AI

34 ×0.8

ECOLO

6 ×0.3

SP

14 ×1.1

MB

Citations per year, relative to Christoph Käding Christoph Käding (= 1×) peers Zhongqi Miao

Countries citing papers authored by Christoph Käding

Since Specialization

Citations

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

Fields of papers citing papers by Christoph Käding

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Christoph Käding

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

All Works

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10 of 10 papers shown

1.

Bodesheim, Paul, et al.. (2022). Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research—Individual identification and attribute prediction with image features from deep neural networks and decoupled decision models applied to elephants and great apes. Mammalian Biology. 102(3). 875–897. 10 indexed citations

2.

Liedtke, Kim Rouven, Christoph Käding, Paula Döring, Sander Bekeschus, & Anne Glitsch. (2021). A case of giant retroperitoneal lymphangioma and IgG4-positive fibrosis: Causality or coincidence?. SHILAP Revista de lepidopterología. 9. 2050313X211016993–2050313X211016993. 1 indexed citations

3.

Käding, Christoph, et al.. (2020). Active and Incremental Learning with Weak Supervision. KI - Künstliche Intelligenz. 34(2). 165–180. 15 indexed citations

4.

Käding, Christoph, et al.. (2019). Active Learning for Deep Object Detection. 181–190. 26 indexed citations

5.

Käding, Christoph, et al.. (2019). Active Learning for Deep Object Detection. 181–190. 36 indexed citations

6.

Käding, Christoph, et al.. (2018). Active Learning for Regression Tasks with Expected Model Output Changes.. British Machine Vision Conference. 103. 8 indexed citations

7.

Burghardt, Tilo, et al.. (2017). Towards Automated Visual Monitoring of Individual Gorillas in the Wild. Explore Bristol Research. 2820–2830. 47 indexed citations

8.

Käding, Christoph, Erik Rodner, Alexander Freytag, & Joachim Denzler. (2016). Watch, Ask, Learn, and Improve: a lifelong learning cycle for visual recognition.. The European Symposium on Artificial Neural Networks. 4 indexed citations

9.

Käding, Christoph, Alexander Freytag, Erik Rodner, Paul Bodesheim, & Joachim Denzler. (2015). Active learning and discovery of object categories in the presence of unnameable instances. 4343–4352. 30 indexed citations

10.

Biedert, Ralf, Andreas Dengel, & Christoph Käding. (2012). Universal eye-tracking based text cursor warping. 361–364. 2 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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