PUB - Publikationen an der Universität Bielefeld

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• [15]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 3001612

  Shoeb Y, Chan RK-W, Schwalbe G, Nowzad A, Güney F, Gottschalk H (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: 7381-7391.

  PUB | DOI | WoS
   
• [14]
  

  2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2989164

  Velioglu R, Chan RK-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 RK-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 3(4): 1381–1405.

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

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982070

  Chan RK-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: 1-10.

  PUB | DOI
   
• [11]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2969466

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

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

  2022 | Dissertation | Veröffentlicht | PUB-ID: 2968878

  Chan RK-W (2022)
  Detecting Anything Overlooked in Semantic Segmentation.
  Bergische Universität Wuppertal.

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

  2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2968876

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

  PUB | DOI
   
• [8]
  

  2021 | Konferenzbeitrag | PUB-ID: 2968879

  Chan RK-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 RK-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 RK-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: 5108-5117.

  PUB | DOI
   
• [5]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968885

  Rottmann M, Maag K, Chan RK-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: 1351-1356.

  PUB | DOI
   
• [4]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968884

  Rottmann M, Colling P, Paul Hack T, Chan RK-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: 1-9.

  PUB | DOI
   
• [3]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968883

  Chan RK-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: 1-8.

  PUB | DOI
   
• [2]
  

  2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968882

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

  PUB | DOI
   
• [1]
  

  2019 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968886

  Chan RK-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: 1395-1403.

  PUB | DOI