# Semi-supervised Skin Detection by Network with Mutual Guidance

arXiv: https://arxiv.org/abs/1908.01977 · HTML: https://mlsanity.com/paper/1908.01977/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Color Invariant Skin Segmentation](https://mlsanity.com/paper/2204.09882/) (2022)
- [Weakly Supervised Human Skin Segmentation using Guidance Attention Mechanisms](https://mlsanity.com/paper/2302.04625/) (2023)
- [Ensembling convolutional neural networks for human skin segmentation](https://mlsanity.com/paper/2407.19310/) (2024)

## Abstract (arXiv)

In this paper 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.