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Title: A CNN model for human parsing based on capacity optimization
Authors: Jiang, Y 
Chi, Z 
Keywords: Capacity optimization
Computational efficiency
Human parsing
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Applied sciences, 2019, v. 9, no. 7, 1330 How to cite?
Journal: Applied sciences 
Abstract: Although a state-of-the-art performance has been achieved in pixel-specific tasks, such as saliency prediction and depth estimation, convolutional neural networks (CNNs) still perform unsatisfactorily in human parsing where semantic information of detailed regions needs to be perceived under the influences of variations in viewpoints, poses, and occlusions. In this paper, we propose to improve the robustness of human parsing modules by introducing a depth-estimation module. A novel scheme is proposed for the integration of a depth-estimation module and a human-parsing module. The robustness of the overall model is improved with the automatically obtained depth labels. As another major concern, the computational efficiency is also discussed. Our proposed human parsing module with 24 layers can achieve a similar performance as the baseline CNN model with over 100 layers. The number of parameters in the overall model is less than that in the baseline model. Furthermore, we propose to reduce the computational burden by replacing a conventional CNN layer with a stack of simplified sub-layers to further reduce the overall number of trainable parameters. Experimental results show that the integration of two modules contributes to the improvement of human parsing without additional human labeling. The proposed model outperforms the benchmark solutions and the capacity of our model is better matched to the complexity of the task.
ISSN: 2076-3417
DOI: 10.3390/app9071330
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access.
The following publication Jiang Y, Chi Z. A CNN Model for Human Parsing Based on Capacity Optimization. Applied Sciences. 2019; 9(7):1330 is available at
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