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|a 10.1109/TIP.2018.2886785
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|a eng
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|a Jiang, Yalong
|e verfasserin
|4 aut
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|a A CNN Model for Semantic Person Part Segmentation with Capacity Optimization
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|c 2018
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a In this paper, a deep learning model with an optimal capacity is proposed to improve the performance of person part segmentation. Previous efforts in optimizing the capacity of a CNN model suffer from a lack of large datasets as well as the over-dependence on a single-modality CNN which is not effective in learning. We make several efforts in addressing these problems. Firstly, other datasets are utilized to train a CNN module for pre-processing image data and a segmentation performance improvement is achieved without a time-consuming annotation process. Secondly, we propose a novel way of integrating two complementary modules to enrich the feature representations for more reliable inferences. Thirdly, the factors to determine the capacity of a CNN model are studied and two novel methods are proposed to adjust (optimize) the capacity of a CNN to match it to the complexity of a task. The over-fitting and under-fitting problems are eased by using our methods. Experimental results show that our model outperforms the state-of-the-art deep learning models with a better generalization ability and a lower computational complexity
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|a Journal Article
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|a Chi, Zheru
|e verfasserin
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2018) vom: 14. Dez.
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|u http://dx.doi.org/10.1109/TIP.2018.2886785
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