FCGNet: Foreground and Class Guided Network for human parsing

Abstract

Understanding the inherent hierarchical human structure is key to human parsing. To capture the human-specific characteristic, it is necessary to focus on the spatial and class information corresponding to the foreground (i.e., human) in an image. Inspired by these insights, we introduce two supervision signals, spatial foreground information and existent class information in the image. By utilizing foreground information as guidance, the network is guided to generate a human-focused feature map and capture the pixel-wise hierarchical characteristics by computing correlations between pixels. Furthermore, we guide the network to consider class information in the image at the feature level and capture the class-wise relationship by calculating correlations between channels. Moreover, during the training phase, we prevent the network from misclassifying pixels into confusing classes by providing the existent class information in the image to the network at the prediction level. Our model achieves state-of-the-art performance with significantly reduced parameters and Multiply-Accumulate Operations (MACs) in three public benchmarks.