Danijel Skočaj

3.7k total citations · 3 hit papers
67 papers, 2.0k citations indexed

About

Danijel Skočaj is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Industrial and Manufacturing Engineering. According to data from OpenAlex, Danijel Skočaj has authored 67 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Computer Vision and Pattern Recognition, 36 papers in Artificial Intelligence and 11 papers in Industrial and Manufacturing Engineering. Recurrent topics in Danijel Skočaj's 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). Danijel Skočaj is often cited by papers focused on Advanced Image and Video Retrieval Techniques (11 papers), Industrial Vision Systems and Defect Detection (11 papers) and Anomaly Detection Techniques and Applications (10 papers). Danijel Skočaj collaborates with scholars based in Slovenia, United Kingdom and Austria. Danijel Skočaj's co-authors include Matej Kristan, Vitjan Zavrtanik, Aleš Leonardis, Domen Tabernik, Sanja Fidler, Horst Bischof, Dejan Tomaževič, Barry Ridge, Peter M. Roth and Michael Zillich and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Construction and Building Materials and IEEE Access.

In The Last Decade

Danijel Skočaj

62 papers receiving 1.9k citations

Hit Papers

DRÆM – A discriminatively... 2020 2026 2022 2024 2021 2020 2021 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. DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection
    2021 447 citations Vitjan Zavrtanik, Matej Kristan et al. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) profile →
  2. Reconstruction by inpainting for visual anomaly detection
    2020 395 citations Vitjan Zavrtanik, Matej Kristan et al. profile →
  3. Mixed supervision for surface-defect detection: From weakly to fully supervised learning
    2021 195 citations Domen Tabernik, Danijel Skočaj 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
Danijel Skočaj Slovenia 20 1.1k 843 492 260 248 67 2.0k
Xinyi Le China 23 467 0.4× 628 0.7× 246 0.5× 222 0.9× 334 1.3× 66 1.9k
Jun Lin China 26 650 0.6× 565 0.7× 89 0.2× 589 2.3× 92 0.4× 157 2.3k
Ralph R. Martin United Kingdom 23 436 0.4× 955 1.1× 186 0.4× 175 0.7× 195 0.8× 57 2.2k
Zhenmin Tang China 27 872 0.8× 1.3k 1.6× 69 0.1× 177 0.7× 98 0.4× 221 2.6k
Wei Xie China 24 473 0.4× 1.0k 1.2× 131 0.3× 269 1.0× 697 2.8× 174 2.4k
Zhiqiang Shen United States 17 1.4k 1.3× 2.2k 2.7× 146 0.3× 102 0.4× 49 0.2× 34 2.9k
Reza Hoseinnezhad Australia 32 1.6k 1.5× 705 0.8× 57 0.1× 895 3.4× 525 2.1× 161 3.2k
Yisen Wang China 16 1.4k 1.3× 661 0.8× 95 0.2× 92 0.4× 64 0.3× 59 1.9k
Hongyuan Zhu Singapore 27 1.1k 1.0× 2.0k 2.3× 71 0.1× 169 0.7× 76 0.3× 77 2.8k
Zsolt Kira United States 22 765 0.7× 1.2k 1.4× 79 0.2× 124 0.5× 158 0.6× 68 1.9k

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
1.
Skočaj, Danijel, et al.. (2025). No label left behind: a unified surface defect detection model for all supervision regimes. Journal of Intelligent Manufacturing.
2.
Zavrtanik, Vitjan, Matej Kristan, & Danijel Skočaj. (2024). Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation. Pattern Recognition Letters. 181. 113–119. 9 indexed citations
3.
Skočaj, Danijel, et al.. (2024). A Methodology for Advanced Manufacturing Defect Detection through Self-Supervised Learning on X-ray Images. Applied Sciences. 14(7). 2785–2785. 3 indexed citations
4.
Tomaževič, Dejan, et al.. (2024). Coupling of unsupervised and supervised deep learning-based approaches for surface anomaly detection. Journal of Electronic Imaging. 33(3). 1 indexed citations
5.
Zavrtanik, Vitjan, Matej Kristan, & Danijel Skočaj. (2024). Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation. 2153–2161. 9 indexed citations
6.
Tabernik, Domen, et al.. (2023). Automated detection and segmentation of cracks in concrete surfaces using joined segmentation and classification deep neural network. Construction and Building Materials. 408. 133582–133582. 16 indexed citations
7.
Zajc, Luka Čehovin, et al.. (2022). Low-Cost Open-Source Robotic Platform for Education. IEEE Transactions on Learning Technologies. 16(1). 18–25. 4 indexed citations
8.
Skočaj, Danijel, et al.. (2022). Video-Based Ski Jump Style Scoring from Pose Trajectory. 682–690. 7 indexed citations
9.
Skočaj, Danijel, et al.. (2022). Segmentation of multiple myeloma plasma cells in microscopy images with noisy labels. 24–24. 5 indexed citations
10.
11.
Zavrtanik, Vitjan, Matej Kristan, & Danijel Skočaj. (2021). DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 8310–8319. 447 indexed citations breakdown →
12.
Avramović, Aleksej, et al.. (2020). Neural-Network-Based Traffic Sign Detection and Recognition in High-Definition Images Using Region Focusing and Parallelization. IEEE Access. 8. 189855–189868. 39 indexed citations
13.
Skočaj, Danijel, Barry Ridge, Matjaž Jogan, et al.. (2019). A System for Continuous Learning of Visual Concepts. University of Birmingham Research Portal (University of Birmingham).
14.
Ridge, Barry, et al.. (2015). Self-Supervised Online Learning of Basic Object Push Affordances. International Journal of Advanced Robotic Systems. 12(3). 7 indexed citations
15.
Kristan, Matej, et al.. (2012). Room classification using a hierarchical representation of space. 1371–1378. 14 indexed citations
16.
Zhou, Kai, et al.. (2011). Visual information abstraction for interactive robot learning. 2. 328–334. 5 indexed citations
17.
Ridge, Barry, Danijel Skočaj, & Aleš Leonardis. (2010). Self-supervised cross-modal online learning of basic object affordances for developmental robotic systems. 5047–5054. 32 indexed citations
18.
Fidler, Sanja, et al.. (2006). Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(3). 337–350. 113 indexed citations
19.
Skočaj, Danijel, Barry Ridge, & Aleš Leonardis. (2006). On different modes of continuous learning of visual properties. 1 indexed citations
20.
Skočaj, Danijel, et al.. (2000). Classification of grapevine cultivars using Kirlian camera and machine learning. Acta agriculturae Slovenica. 75(1). 133–138. 6 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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