J.J. van Hees

1.1k total citations · 1 hit paper
21 papers, 528 citations indexed

About

J.J. van Hees is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, J.J. van Hees has authored 21 papers receiving a total of 528 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 4 papers in Media Technology. Recurrent topics in J.J. van Hees's work include Advanced Image and Video Retrieval Techniques (4 papers), Multimodal Machine Learning Applications (4 papers) and Remote-Sensing Image Classification (3 papers). J.J. van Hees is often cited by papers focused on Advanced Image and Video Retrieval Techniques (4 papers), Multimodal Machine Learning Applications (4 papers) and Remote-Sensing Image Classification (3 papers). J.J. van Hees collaborates with scholars based in Germany, United States and Spain. J.J. van Hees's co-authors include Andreas Dengel, Federico Raue, Stanislav Frolov, Tobias Hinz, Georg Buscher, Ralf Biedert, Sven Schwarz, Damian Borth, Patrick Helber and Tim Althoff and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neural Networks and Earth system science data.

In The Last Decade

J.J. van Hees

20 papers receiving 508 citations

Hit Papers

align trajectories

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.

Audioclip: Extending Clip to Image, Text and Audio

2022 163 citations Federico Raue, J.J. van Hees et al. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) profile →

Peers

J.J. van Hees

CVPR

AI

HI

SP

CN

Camilla Forsell Sweden

Angus G. Forbes United States

In Seop Na South Korea

Sarah Taylor United Kingdom

Robert Bryll United States

Motaz El-Saban Egypt

Shagan Sah United States

Rui Qian China

Raid Rafi Omar Al-Nima Iraq

Konstantinos Rapantzikos Greece

Camilla Forsell Sweden

J.J. van Hees

361 ×1.4

CVPR

107 ×0.7

AI

74 ×0.8

HI

65 ×0.7

SP

44 ×1.1

CN

Citations per year, relative to J.J. van Hees J.J. van Hees (= 1×) peers Camilla Forsell

Countries citing papers authored by J.J. van Hees

Since Specialization

Citations

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

Fields of papers citing papers by J.J. van Hees

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J.J. van Hees. 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 J.J. van Hees. The network helps show where J.J. van Hees may publish in the future.

Co-authorship network of co-authors of J.J. van Hees

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Schulz, Christian, Patrick Helber, Benjamin Bischke, et al.. (2023). TreeSatAI Benchmark Archive : a multi-sensor, multi-label dataset for tree species classification in remote sensing. Earth system science data. 15(2). 681–695. 36 indexed citations

2.

Raue, Federico, et al.. (2023). Sequential Spatial Transformer Networks for Salient Object Classification. 328–335.

3.

Raue, Federico, et al.. (2023). Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(8). 9862–9882. 33 indexed citations

4.

Hees, J.J. van, et al.. (2023). Improving Natural Language Inference in Arabic Using Transformer Models and Linguistically Informed Pre-Training. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 318–322. 2 indexed citations

5.

Raue, Federico, et al.. (2022). Audioclip: Extending Clip to Image, Text and Audio. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 976–980. 163 indexed citations breakdown →

6.

Raue, Federico, et al.. (2022). Less is More: Proxy Datasets in NAS approaches. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 1952–1960. 3 indexed citations

7.

Raue, Federico, et al.. (2022). Self-supervised Test-time Adaptation on Video Data. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2603–2612. 11 indexed citations

8.

Frolov, Stanislav, Tobias Hinz, Federico Raue, J.J. van Hees, & Andreas Dengel. (2021). Adversarial text-to-image synthesis: A review. Neural Networks. 144. 187–209. 129 indexed citations

9.

Frolov, Stanislav, et al.. (2020). Leveraging Visual Question Answering to Improve Text-to-Image Synthesis. arXiv (Cornell University). 17–22. 1 indexed citations

10.

Frolov, Stanislav, et al.. (2020). Hybrid Sequence to Sequence Model for Video Object Segmentation.. arXiv (Cornell University). 1 indexed citations

11.

Hees, J.J. van, et al.. (2020). The Focus–Aspect–Value model for predicting subjective visual attributes. International Journal of Multimedia Information Retrieval. 9(1). 47–60. 1 indexed citations

12.

Helber, Patrick, Benjamin Bischke, J.J. van Hees, & Andreas Dengel. (2019). Towards a Sentinel-2 Based Human Settlement Layer. 5936–5939. 7 indexed citations

13.

Hees, J.J. van, et al.. (2019). The Focus-Aspect-Value Model for Explainable Prediction of Subjective Visual Interpretation. 16–24. 1 indexed citations

14.

Bischke, Benjamin, Patrick Helber, J.J. van Hees, & Andreas Dengel. (2019). Location-Specific Embedding Learning for the Semantic Segmentation of Building Footprints on a Global Scale. 2 indexed citations

15.

Helber, Patrick, Benjamin Bischke, Qiushi Guo, J.J. van Hees, & Andreas Dengel. (2019). Multi-Scale Machine Learning for the Classification of Building Property Values. 4873–4876. 4 indexed citations

16.

Althoff, Tim, Damian Borth, J.J. van Hees, & Andreas Dengel. (2013). Analysis and forecasting of trending topics in online media streams. 907–916. 28 indexed citations

17.

Biedert, Ralf, J.J. van Hees, Andreas Dengel, & Georg Buscher. (2012). A robust realtime reading-skimming classifier. 123–130. 51 indexed citations

18.

Hees, J.J. van, Thomas Roth–Berghofer, & Andreas Dengel. (2010). Linked Data Games: Simulating Human Association with Linked Data.. LWA. 255–260. 1 indexed citations

19.

Biedert, Ralf, Georg Buscher, Sven Schwarz, J.J. van Hees, & Andreas Dengel. (2010). Text 2.0. 4003–4008. 43 indexed citations

20.

Borth, Damian, J.J. van Hees, Markus Koch, et al.. (2009). TubeFiler. 1111–1112. 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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