Publications as First Author

1. Limberg, C., Harter, A., Melnik, A., Ritter, H., & Prendinger, H. (2024). Deep Detection Dreams: Enhancing Visualization Tools for Single Stage Object Detectors. Springer Communications in Computer and Information Science.

   @article{limberg2024deepdetection,
     author = {Limberg, Christian and Harter, Augustin and Melnik, Andrew and Ritter, Helge and Prendinger, Helmut},
     title = {Deep Detection Dreams: Enhancing Visualization Tools for Single Stage Object Detectors},
     journal = {Springer Communications in Computer and Information Science},
     year = {2024}
   }
   

   
   
2. Limberg, C., & Zhang, Z. (2024). Mapping the Audio Landscape for Innovative Music Sample Generation. ACM International Conference on Multimedia Retrieval.

   @inproceedings{limberg2024mapping,
     title = {Mapping the Audio Landscape for Innovative Music Sample Generation},
     author = {Limberg, Christian and Zhang, Zhe},
     booktitle = {ACM International Conference on Multimedia Retrieval},
     year = {2024}
   }
   

   
   
3. Limberg, C., Goncalves, A., Rigault, B., & Prendinger, H. (2024). Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and Action Recognition in Drone Imagery. ICRA 2024 First Workshop on Vision-Language Models for Navigation and Manipulation.

   @inproceedings{limberg2024leveraging,
     title = {Leveraging YOLO-World and GPT-4V LMMs for Zero-Shot Person Detection and Action Recognition in Drone Imagery},
     author = {Limberg, Christian and Goncalves, Artur and Rigault, Bastien and Prendinger, Helmut},
     booktitle = {ICRA 2024 First Workshop on Vision-Language Models for Navigation and Manipulation},
     year = {2024}
   }
   

   
   
4. Limberg, C., Melnik, A., Ritter, H., & Prendinger, H. (2023). YOLO: You Only Look 10647 Times. Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.

   @inproceedings{limberg2023yolo,
     title = {YOLO: You Only Look 10647 Times},
     author = {Limberg, Christian and Melnik, Andrew and Ritter, Helge and Prendinger, Helmut},
     booktitle = {Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
     year = {2023},
     organization = {SCITEPRESS-Science and Technology Publications}
   }
   

   
   
5. Limberg, C. (2022). Competence Modeling for Human-Robot Cooperation (p. 146) [PhD thesis, Universität Bielefeld]. https://doi.org/10.4119/unibi/2962486
   

   Abstract: Autonomous robots are going to become an increasingly important part of humans’ everyday life. Currently, not many tasks could be automated completely, so it is still necessary, and sometimes even preferable, to take the human in the loop. There is a wide range of unsolved challenges in such a Human Robot Cooperation. One of them is the modeling of competence, which concerns the estimation of the capabilities of both interaction partners towards a common goal. From a robot’s point of view, this does not only mean awareness of its own but also of human capabilities.
   
   The thesis focuses on competence modeling regarding active and incremental classification. Within a generic scenario where the human uses his world knowledge about the environment for teaching the robot, we have identified three different areas where competence modeling is elementary:
   
   First, the robot should be able to assess its own capabilities. We show that it is possible to estimate classification accuracy in a semi-supervised manner using a static regression model trained on histograms of confidences computed on unlabeled samples in active learning.
   
   Second, the robot should assess its teacher’s capabilities because sometimes the human teacher does not have perfect domain knowledge. We propose an additional model that is, by excluding ambiguous areas in feature space, capable of increasing training speed by showing uncertain but valuable samples to the user for labeling.
   
   Finally, bringing the two complementary partners together is the last cornerstone of this thesis. We introduce an active learning querying technique utilizing dimension-reduction approaches for realizing a visualization of deep image feature spaces for the human. Non-expert humans can label robot object recordings faster and more qualitatively using this new teaching interface. That was revealed by a user study we conducted. However, the proposed method adds specific demands to the Machine Learning model for handling non-i.i.d. training data. Therefore, we investigate several approaches enabling instance-based classifier for compensating these conditions.

   @phdthesis{2962486,
     author = {Limberg, Christian},
     pages = {146},
     publisher = {Universität Bielefeld},
     title = {{Competence Modeling for Human-Robot Cooperation}},
     doi = {10.4119/unibi/2962486},
     year = {2022}
   }
   

   
   
6. Limberg, C., Melnik, A., Harter, A., & Ritter, H. (2022). YOLO–You only look 10647 times. ArXiv Preprint ArXiv:2201.06159.

   @article{limberg2022yolo,
     title = {YOLO--You only look 10647 times},
     author = {Limberg, Christian and Melnik, Andrew and Harter, Augustin and Ritter, Helge},
     journal = {arXiv preprint arXiv:2201.06159},
     year = {2022}
   }
   

   
   
7. Limberg, C., Wersing, H., & Ritter, H. (2020, November). Accuracy Estimation for an Incrementally Learning Cooperative Inventory Assistant Robot. International Conference on Neural Information Processing (ICONIP).

   @inproceedings{limberg2020accuracy,
     author = {Limberg, Christian and Wersing, Heiko and Ritter, Helge},
     title = {Accuracy Estimation for an Incrementally Learning Cooperative Inventory Assistant Robot},
     booktitle = {International Conference on Neural Information Processing (ICONIP)},
     year = {2020},
     month = nov
   }
   

   
   
8. Limberg, C., Göpfert, J., Wersing, H., & Ritter, H. (2020, October). Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. International Conference on Artificial Neural Networks (ICANN).

   @inproceedings{limberg2020prototype,
     author = {Limberg, Christian and Göpfert, Jan and Wersing, Heiko and Ritter, Helge},
     title = {Prototype-Based Online Learning on Homogeneously Labeled Streaming Data},
     booktitle = {International Conference on Artificial Neural Networks (ICANN)},
     year = {2020},
     month = oct
   }
   

   
   
9. Limberg, C., Wersing, H., & Ritter, H. (2020). Beyond Cross-Validation - Accuracy Estimation for Incremental and Active Learning Models. Machine Learning and Knowledge Extraction (MAKE), 2(3), 327–346. https://doi.org/10.3390/make2030018

   @article{limberg2020beyond,
     author = {Limberg, Christian and Wersing, Heiko and Ritter, Helge},
     title = {Beyond Cross-Validation - Accuracy Estimation for Incremental and
     Active Learning Models},
     journal = {Machine Learning and Knowledge Extraction (MAKE)},
     volume = {2},
     number = {3},
     pages = {327--346},
     year = {2020},
     month = sep,
     doi = {10.3390/make2030018}
   }
   

   
   
10. Limberg, C. (2020). CUPSNBOTTLES. https://doi.org/10.21227/ywwz-cb26

    @misc{limberg2020cupsnbottles,
      author = {Limberg, Christian},
      note = {IEEE Dataport},
      title = {CUPSNBOTTLES},
      year = {2020},
      doi = {10.21227/ywwz-cb26},
      url = {http://dx.doi.org/10.21227/ywwz-cb26}
    }
    

    
    
11. Limberg, C., Krieger, K., Wersing, H., & Ritter, H. (2019). Active Learning for Image Recognition Using a Visualization-Based User Interface. International Conference on Artificial Neural Networks (ICANN), 495–506.

    @inproceedings{limberg2019active,
      author = {Limberg, Christian and Krieger, Kathrin and Wersing, Heiko and Ritter, Helge},
      title = {Active Learning for Image Recognition Using a Visualization-Based
      User Interface},
      booktitle = {International Conference on Artificial Neural Networks (ICANN)},
      pages = {495--506},
      year = {2019},
      month = sep
    }
    

    
    
12. Limberg, C., Wersing, H., & Ritter, H. (2018, October). Improving Active Learning by Avoiding Ambiguous Samples. International Conference on Artificial Neural Networks (ICANN).

    @inproceedings{limberg2018improving,
      title = {Improving Active Learning by Avoiding Ambiguous Samples},
      author = {Limberg, Christian and Wersing, Heiko and Ritter, Helge},
      year = {2018},
      month = oct,
      publisher = {Springer},
      booktitle = {International Conference on Artificial Neural Networks (ICANN)}
    }
    

    
    
13. Limberg, C., Wersing, H., & Ritter, H. (2018). Efficient Accuracy Estimation for Instance-Based Incremental Active Learning. European Symposium on Artificial Neural Networks (ESANN), 171–176.

    @inproceedings{limberg2018efficient,
      author = {Limberg, Christian and Wersing, Heiko and Ritter, Helge},
      title = {Efficient Accuracy Estimation for Instance-Based Incremental Active Learning},
      booktitle = {European Symposium on Artificial Neural Networks (ESANN)},
      pages = {171--176},
      year = {2018},
      month = apr
    }