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Conference Paper

Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation

MPS-Authors
/persons/resource/persons249705

Feng,  Haiwen
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons217595

Bolkart,  Timo
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84254

Tesch,  Joachim
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons75293

Black,  Michael J.       
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons289140

Abrevaya,  Victoria
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://doi.org/10.1007/978-3-031-19778-9_5
(Publisher version)

https://doi.org/10.48550/arXiv.2205.03962
(Preprint)

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Citation

Feng, H., Bolkart, T., Tesch, J., Black, M. J., & Abrevaya, V. (2022). Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation. In S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, & T. Hassner (Eds.), Computer Vision – ECCV 2022 (pp. 72-90). Cham: Springer. doi:10.1007/978-3-031-19778-9_5.


Cite as: https://hdl.handle.net/21.11116/0000-000D-416F-E
Abstract
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