PUB - Publikationen an der Universität Bielefeld

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• [13]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982070

  Chan, R.K.-W., Penquitt, S., Gottschalk, H.: LU-Net: Invertible Neural Networks Based on Matrix Factorization. 2023 International Joint Conference on Neural Networks (IJCNN). p. 1-10. IEEE, Piscataway, NJ (2023).

  PUB | DOI
   
• [12]
  

  2023 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2968966

  Chan, R.K.-W., Dardashti, R., Osinski, M., Rottmann, M., Brüggemann, D., Rücker, C., Schlicht, P., Hüger, F., Rummel, N., Gottschalk, H.: What should AI see? Using the public’s opinion to determine the perception of an AI. AI and Ethics. (2023).

  PUB | DOI | Download (ext.) | arXiv | Preprint
   
• [11]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969466

  Maag, K., Chan, R.K.-W., Uhlemeyer, S., Kowol, K., Gottschalk, H.: Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. In: Wang, L., Gall, J., Chin, T.-J., Sato, I., and Chellappa, R. (eds.) Computer Vision – ACCV 2022. 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part V. Lecture Notes in Computer Science. 13845, p. 476-494. Springer Nature Switzerland, Cham (2023).

  PUB | DOI | Download (ext.)
   
• [10]
  

  2022 | Dissertation | Veröffentlicht | PUB-ID: 2968878

  Chan, R.K.-W.: Detecting Anything Overlooked in Semantic Segmentation. Bergische Universität Wuppertal (2022).

  PUB | DOI | Download (ext.)
   
• [9]
  

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2968876

  Chan, R.K.-W., Uhlemeyer, S., Rottmann, M., Gottschalk, H.: Detecting and Learning the Unknown in Semantic Segmentation. In: Fingscheidt, T., Gottschalk, H., and Houben, S. (eds.) Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety. p. 277-313. Springer International Publishing, Cham (2022).

  PUB | DOI
   
• [8]
  

  2021 | Konferenzbeitrag | PUB-ID: 2968879

  Chan, R.K.-W., Lis, K., Uhlemeyer, S., Blum, H., Honari, S., Siegwart, R., Fua, P., Salzmann, M., Rottmann, M.: SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation. Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks. (2021).

  PUB | Download (ext.) | arXiv
   
• [7]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968881

  Brüggemann, D., Chan, R.K.-W., Gottschalk, H., Bracke, S.: Software architecture for human- centered reliability assessment for neural networks in autonomous. Proc. of the 11th IMA International Conference on Modelling in Industrial Maintenance and Reliability. Institute of Mathematics & its Applications (2021).

  PUB | DOI
   
• [6]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968880

  Chan, R.K.-W., Rottmann, M., Gottschalk, H.: Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). p. 5108-5117. IEEE (2021).

  PUB | DOI
   
• [5]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968885

  Rottmann, M., Maag, K., Chan, R.K.-W., Huger, F., Schlicht, P., Gottschalk, H.: Detection of False Positive and False Negative Samples in Semantic Segmentation. 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). p. 1351-1356. IEEE (2020).

  PUB | DOI
   
• [4]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968884

  Rottmann, M., Colling, P., Paul Hack, T., Chan, R.K.-W., Huger, F., Schlicht, P., Gottschalk, H.: Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. 2020 International Joint Conference on Neural Networks (IJCNN). p. 1-9. IEEE (2020).

  PUB | DOI
   
• [3]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968883

  Chan, R.K.-W., Rottmann, M., Huger, F., Schlicht, P., Gottschalk, H.: Controlled False Negative Reduction of Minority Classes in Semantic Segmentation. 2020 International Joint Conference on Neural Networks (IJCNN). p. 1-8. IEEE (2020).

  PUB | DOI
   
• [2]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968882

  Chan, R.K.-W., Rottmann, M., Gottschalk, H., Hüger, F., Schlicht, P.: Application of Maximum Likelihood Decision Rules for Handling Class Imbalance in Semantic Segmentation. In: Baraldi, P., Maio, F.D., and Zio, E. (eds.) Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference. p. 3065-3072. Research Publishing Services, Singapore (2020).

  PUB | DOI
   
• [1]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968886

  Chan, R.K.-W., Rottmann, M., Dardashti, R., Huger, F., Schlicht, P., Gottschalk, H.: The Ethical Dilemma When (Not) Setting up Cost-Based Decision Rules in Semantic Segmentation. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). p. 1395-1403. IEEE (2019).

  PUB | DOI