Semi-Supervised Skin Detection by Network With Mutual Guidance

Yi He, Jiayuan Shi, Chuan Wang, Haibin Huang, Jiaming Liu, Guanbin Li, Risheng Liu, Jue Wang; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 2111-2120

Abstract


We present a new data-driven method for robust skin detection from a single human portrait image. Unlike previous methods, we incorporate human body as a weak semantic guidance into this task, considering acquiring large-scale of human labeled skin data is commonly expensive and time-consuming. To be specific, we propose a dual-task neural network for joint detection of skin and body via a semi-supervised learning strategy. The dual-task network contains a shared encoder but two decoders for skin and body separately. For each decoder, its output also serves as a guidance for its counterpart, making both decoders mutually guided. Extensive experiments were conducted to demonstrate the effectiveness of our network with mutual guidance, and experimental results show our network outperforms the state-of-the-art in skin detection.

Related Material


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[bibtex]
@InProceedings{He_2019_ICCV,
author = {He, Yi and Shi, Jiayuan and Wang, Chuan and Huang, Haibin and Liu, Jiaming and Li, Guanbin and Liu, Risheng and Wang, Jue},
title = {Semi-Supervised Skin Detection by Network With Mutual Guidance},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}