PUB - Publikationen an der Universität Bielefeld

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• [15]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001612

  Shoeb, Y., Chan, R.K.-W., Schwalbe, G., Nowzad, A., Güney, F., Gottschalk, H.: Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes. 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE Winter Conference on Applications of Computer Vision. p. 7381-7391. IEEE, Los Alamitos (2024).

  PUB | DOI | WoS
   
• [14]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2989164

  Velioglu, R., Chan, R.K.-W., Hammer, B.: FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation. 2024 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers (IEEE), New York (2024).

  PUB | DOI | WoS | arXiv
   
• [13]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | 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. 3, 1381–1405 (2023).

  PUB | PDF | DOI | Download (ext.) | arXiv | Preprint
   
• [12]
  

  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
   
• [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