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. (2023): LU-Net: Invertible Neural Networks Based on Matrix Factorization. In: 2023 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE. S. 1-10.

  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. (2023): What should AI see? Using the public’s opinion to determine the perception of an AI AI and Ethics

  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. (2023): Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. In: Lei Wang; Juergen Gall; Tat-Jun Chin; Imari Sato; Rama Chellappa (Hrsg.): Computer Vision – ACCV 2022. 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part V. Cham: Springer Nature Switzerland. (Lecture Notes in Computer Science, 13845). S. 476-494.

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

  2022 | Dissertation | Veröffentlicht | PUB-ID: 2968878

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

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

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2968876

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

  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. (2021): SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation. In: Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks.

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

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968881

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

  PUB | DOI
   
• [6]
  

  2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968880

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

  PUB | DOI
   
• [5]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968885

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

  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. (2020): Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE. S. 1-9.

  PUB | DOI
   
• [3]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968883

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

  PUB | DOI
   
• [2]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968882

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

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• [1]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968886

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

  PUB | DOI