PUB - Publikationen an der Universität Bielefeld

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• [15]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001612

  Shoeb, Y., et al., 2024. Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes. In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE Winter Conference on Applications of Computer Vision. Los Alamitos: IEEE, pp. 7381-7391.

  PUB | DOI | WoS
   
• [14]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2989164

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

  PUB | DOI | WoS | arXiv
   
• [13]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2968966

  Chan, R.K.-W., et al., 2023. What should AI see? Using the public’s opinion to determine the perception of an AI. AI and Ethics, 3(4), p 1381–1405.

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

  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, pp. 1-10.

  PUB | DOI
   
• [11]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969466

  Maag, K., et al., 2023. Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. In L. Wang, et al., 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. no.13845 Cham: Springer Nature Switzerland, pp. 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., et al., 2022. Detecting and Learning the Unknown in Semantic Segmentation. In T. Fingscheidt, H. Gottschalk, & S. Houben, eds. Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety. Cham: Springer International Publishing, pp. 277-313.

  PUB | DOI
   
• [8]
  

  2021 | Konferenzbeitrag | PUB-ID: 2968879

  Chan, R.K.-W., et al., 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., et al., 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, pp. 5108-5117.

  PUB | DOI
   
• [5]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968885

  Rottmann, M., et al., 2020. Detection of False Positive and False Negative Samples in Semantic Segmentation. In 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, pp. 1351-1356.

  PUB | DOI
   
• [4]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968884

  Rottmann, M., et al., 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, pp. 1-9.

  PUB | DOI
   
• [3]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968883

  Chan, R.K.-W., et al., 2020. Controlled False Negative Reduction of Minority Classes in Semantic Segmentation. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8.

  PUB | DOI
   
• [2]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968882

  Chan, R.K.-W., et al., 2020. Application of Maximum Likelihood Decision Rules for Handling Class Imbalance in Semantic Segmentation. In P. Baraldi, F. D. Maio, & E. Zio, eds. Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference. Singapore: Research Publishing Services, pp. 3065-3072.

  PUB | DOI
   
• [1]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968886

  Chan, R.K.-W., et al., 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, pp. 1395-1403.

  PUB | DOI