PUB - Publikationen an der Universität Bielefeld

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• [15]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001612

  Y. Shoeb, et al., “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, Los Alamitos: IEEE, 2024, pp.7381-7391.

  PUB | DOI | WoS
   
• [14]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2989164

  R. Velioglu, R.K.-W. Chan, and B. Hammer, “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), New York: Institute of Electrical and Electronics Engineers (IEEE), 2024.

  PUB | DOI | WoS | arXiv
   
• [13]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2968966

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

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

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982070

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

  PUB | DOI
   
• [11]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969466

  K. Maag, et al., “Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects”, Computer Vision – ACCV 2022. 16th Asian Conference on Computer Vision, Macao, China, December 4–8, 2022, Proceedings, Part V, L. Wang, et al., eds., Lecture Notes in Computer Science, vol. 13845, Cham: Springer Nature Switzerland, 2023, pp.476-494.

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

  2022 | Dissertation | Veröffentlicht | PUB-ID: 2968878

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

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

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2968876

  R.K.-W. Chan, et al., “Detecting and Learning the Unknown in Semantic Segmentation”, Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety, T. Fingscheidt, H. Gottschalk, and S. Houben, eds., Cham: Springer International Publishing, 2022, pp.277-313.

  PUB | DOI
   
• [8]
  

  2021 | Konferenzbeitrag | PUB-ID: 2968879

  R.K.-W. Chan, et al., “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

  D. Brüggemann, et al., “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

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

  PUB | DOI
   
• [5]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968885

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

  PUB | DOI
   
• [4]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968884

  M. Rottmann, et al., “Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities”, 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020, pp.1-9.

  PUB | DOI
   
• [3]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968883

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

  PUB | DOI
   
• [2]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968882

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

  PUB | DOI
   
• [1]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968886

  R.K.-W. Chan, et al., “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), IEEE, 2019, pp.1395-1403.

  PUB | DOI