Robin Chan

Orcid: 0000-0003-2935-3275

Affiliations:

Technical University of Berlin, Germany
Bielefeld University, Germany (2022 - 2024)
University of Wuppertal, Germany (PhD 2022)

According to our database¹, Robin Chan authored at least 20 papers between 2019 and 2025.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

Legend:

Book In proceedings Article PhD thesis Dataset Other

Bibliography

2025

Enhancing Person-to-Person Virtual Try-On with Multi-Garment Virtual Try-Off.

[BibT_eX]

[DOI]

CoRR, April, 2025

MGT: Extending Virtual Try-Off to Multi-Garment Scenarios.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV 2025, 2025

2024

TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models.

[BibT_eX]

[DOI]

CoRR, 2024

New advances in universal approximation with neural networks of minimal width.

[BibT_eX]

[DOI]

Dennis Rochau

Robin Chan

Hanno Gottschalk

CoRR, 2024

Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation.

[BibT_eX]

[DOI]

Riza Velioglu

Robin Chan

Barbara Hammer

Proceedings of the International Joint Conference on Neural Networks, 2024

2023

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

[BibT_eX]

[DOI]

AI Ethics, November, 2023

LU-Net: Invertible Neural Networks Based on Matrix Factorization.

[BibT_eX]

[DOI]

Robin Chan

Sarina Penquitt

Hanno Gottschalk

Proceedings of the International Joint Conference on Neural Networks, 2023

2022

Detecting Anything Overlooked in Semantic Segmentation.

[BibT_eX]

[DOI]

Robin Chan

PhD thesis, 2022

Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Detecting and Learning the Unknown in Semantic Segmentation.

[BibT_eX]

[DOI]

CoRR, 2022

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

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ACCV 2022, 2022

2021

SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

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

[BibT_eX]

[DOI]

Robin Chan

Matthias Rottmann

Hanno Gottschalk

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020

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

[BibT_eX]

[DOI]

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Controlled False Negative Reduction of Minority Classes in Semantic Segmentation.

[BibT_eX]

[DOI]

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Detection of False Positive and False Negative Samples in Semantic Segmentation.

[BibT_eX]

[DOI]

Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020

2019

MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation.

[BibT_eX]

[DOI]

CoRR, 2019

Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation.

[BibT_eX]

[DOI]

CoRR, 2019

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

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019

Robin Chan

Timeline

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