FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation

Velioglu R, Chan RK-W, Hammer B (2024)
arXiv:2404.08582.

What should AI see? Using the public’s opinion to determine the perception of an AI

Chan RK-W, Dardashti R, Osinski M, Rottmann M, Brüggemann D, Rücker C, Schlicht P, Hüger F, Rummel N, Gottschalk H (2023)
AI and Ethics 3(4): 1381–1405.

LU-Net: Invertible Neural Networks Based on Matrix Factorization

Chan RK-W, Penquitt S, Gottschalk H (2023)
In: 2023 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE: 1-10.

Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects

Maag K, Chan RK-W, Uhlemeyer S, Kowol K, Gottschalk H (2023)
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.

Detecting Anything Overlooked in Semantic Segmentation

Chan RK-W (2022)
Bergische Universität Wuppertal.

Detecting and Learning the Unknown in Semantic Segmentation

Chan RK-W, Uhlemeyer S, Rottmann M, Gottschalk H (2022)
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.

SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Chan RK-W, Lis K, Uhlemeyer S, Blum H, Honari S, Siegwart R, Fua P, Salzmann M, Rottmann M (2021)
In: Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks.

Software architecture for human- centered reliability assessment for neural networks in autonomous

Brüggemann D, Chan RK-W, Gottschalk H, Bracke S (2021)
In: Proc. of the 11th IMA International Conference on Modelling in Industrial Maintenance and Reliability. Institute of Mathematics & its Applications.

Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation

Chan RK-W, Rottmann M, Gottschalk H (2021)
In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE: 5108-5117.

Detection of False Positive and False Negative Samples in Semantic Segmentation

Rottmann M, Maag K, Chan RK-W, Huger F, Schlicht P, Gottschalk H (2020)
In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE: 1351-1356.

Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities

Rottmann M, Colling P, Paul Hack T, Chan RK-W, Huger F, Schlicht P, Gottschalk H (2020)
In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-9.

Controlled False Negative Reduction of Minority Classes in Semantic Segmentation

Chan RK-W, Rottmann M, Huger F, Schlicht P, Gottschalk H (2020)
In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE: 1-8.

Application of Maximum Likelihood Decision Rules for Handling Class Imbalance in Semantic Segmentation

Chan RK-W, Rottmann M, Gottschalk H, Hüger F, Schlicht P (2020)
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.

The Ethical Dilemma When (Not) Setting up Cost-Based Decision Rules in Semantic Segmentation

Chan RK-W, Rottmann M, Dardashti R, Huger F, Schlicht P, Gottschalk H (2019)
In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE: 1395-1403.