Semi-Supervised Skin Detection by Network With Mutual Guidance

Abstract

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 dualtask 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.

An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection

“…According to the analysis of the experimental results, the performance rates of the proposed scheme are 95.40%, 92.33%, and 4.60% for the true positive rate, accuracy, and false negative rate, respectively. In recent years, many studies have reported of high performance in the harmful content detections using various deep learning approaches [6][7][8][9][10][11]13,14]. Among the studies, some use video frame image or video clips [7][8][9][10][11], motion analysis [6], or age prediction from facial images [14] as the visual element of input content to determine the harmfulness.…”

“…When comparing the performance results of these approaches, the approach of [6] with the accuracy rate of 95.1% and the approach of [7] with the true positive rates of 97.52% are showed better performance than the enhanced multimodal stacking scheme suggested in this study. However, since the techniques used in [6][7][8][9][10][11]13,14] cannot properly detect the harmful contents based on the acoustic elements, the proposed scheme in this paper, which includes an auditory element detection, can be evaluated as more advanced.…”

“…Since much of the recent pornographic contents provided through the online personal broadcasting services have harmful scenes either in the form of visual or auditory manner, fast and accurate detection seems vital. However, most previous studies related to the automatic detection of pornographic contents have mainly focused on single modal detection that extracts and uses either visual or auditory features [3][4][5][6][7][8][9][10][11][12][13][14]. One of the limitations of existing methods based on single modal detection approach is that the harmful contents without the detectable elements cannot be detected.…”

“…Recently, to supplement the problems of the existing studies [3][4][5][6][7][8][9][10][11][12][13][14][15][16] and satisfy the requirements of the online harmful content detection, a method that utilizes multiple features extracted from the visual, motion, and auditory elements for the pornographic detections was proposed in [17]. In the detection method proposed in [17], the harmfulness is decided via unit segments that are divided as a particular unit length from the input content.…”

Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression and Semi-Supervised Domain Adaptation

“…Content may change prior to final publication. ject detection [55], and skin detection in a single human portrait image [56]. Domain adaptation is a learning task that enables an inference model on a source domain to perform on a target domain, when the data distributions between the two domains are different.…”