A CNN Model for Semantic Person Part Segmentation With Capacity Optimization

Yalong Jiang, Zheru Chi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


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 convolutional neural network (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. First, 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. Second, we propose a novel way of integrating two complementary modules to enrich the feature representations for more reliable inferences. Third, 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.

Original languageEnglish
Article number8576539
Pages (from-to)2465-2478
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number5
Publication statusPublished - May 2019


  • capacity optimization
  • complementary modules
  • convolutional neural network
  • Person part segmentation
  • simplification of CNNs

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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