Semi-supervised Skin Detection by Network with Mutual Guidance
This addresses the problem of expensive and time-consuming skin data labeling for researchers in computer vision, though it appears incremental.
The paper tackles robust skin detection from single human portrait images by incorporating human body as weak semantic guidance, and results show their network outperforms state-of-the-art methods.
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.