Danijel Skočaj

62 papers receiving 1.9k citations

Hit Papers

DRÆM – A discriminatively trained reconstruction embeddin...2020202620222024202120202021100200300400

Peers

Danijel Skočaj
Comparison fields: 5 of 113
  • Artificial Intelligence 1.1k
  • Computer Vision and Pattern Recognition 843
  • Industrial and Manufacturing Engineering 492
  • Computer Networks and Communications 260
  • Control and Systems Engineering 248
Replace Ralph R. Martin with:
Ralph R. Martin United Kingdom
Hao Gao China
Xinyi Le China
Zhenmin Tang China
Zhiqiang Shen United States
Hongfeng Tao China
N.H.C. Yung Hong Kong
Jun Lin China
Hongyuan Zhu Singapore
Franz Pernkopf Austria
Danijel Skočaj relative to Ralph R. Martin United Kingdom Ralph R. Martin's profile →
Citations per field
00.5×10×14.1×
Ralph R. Martin · 1×
Citations per year

Countries citing papers authored by Danijel Skočaj

Since Specialization
Citations

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

Fields of papers citing papers by Danijel Skočaj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Danijel Skočaj

This figure shows the co-authorship network connecting the top 25 collaborators of Danijel Skočaj. A scholar is included among the top collaborators of Danijel Skočaj 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 Danijel Skočaj. Danijel Skočaj 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
#WorkIndexed citations
1 0
2 9
3 3
4 1
5 2
6 16
7 4
8 7
9 0
10
DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detectionbreakdown →
447
11 39
12 4
13 14
14 32
15
A basic cognitive system for interactive continuous learning of visual concepts
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16 29
17 30
18 113
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On different modes of continuous learning of visual properties
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20 6

About Danijel Skočaj

Danijel Skočaj is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Industrial and Manufacturing Engineering, having authored 67 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (11 papers), Industrial Vision Systems and Defect Detection (11 papers) and Anomaly Detection Techniques and Applications (10 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (492 citations), Computer Vision and Pattern Recognition (843 citations) and Artificial Intelligence (1.1k citations). Danijel Skočaj has collaborated with scholars based in Slovenia, United Kingdom and Austria. Frequent co-authors include Matej Kristan, Vitjan Zavrtanik, Aleš Leonardis, Domen Tabernik, Sanja Fidler, Dejan Tomaževič, Horst Bischof, Barry Ridge, Peter M. Roth and Michael Zillich. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Construction and Building Materials and IEEE Access.

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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