Semantic segmentation of fashion photos using light-weight asymmetric U-Net

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Semantic segmentation is crucial for machine image understanding. The availability of public data set such as MSCOCO, ADE20K and CityScape encourages the development of popular models for semantic segmentation like SegNet and PSPNet. In this paper, we propose a light-weight deep neural network for street-fashion semantic segmentation. Experiment on ModaNet data set shows that our proposed network results in high accuracy despite its low requirement in the computational resource.

Original languageEnglish
Title of host publication2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages175-178
Number of pages4
ISBN (Electronic)9781728135755
DOIs
Publication statusPublished - 2019 Oct
Event8th IEEE Global Conference on Consumer Electronics, GCCE 2019 - Osaka, Japan
Duration: 2019 Oct 152019 Oct 18

Publication series

Name2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019

Conference

Conference8th IEEE Global Conference on Consumer Electronics, GCCE 2019
CountryJapan
CityOsaka
Period19/10/1519/10/18

Keywords

  • Fashion photos
  • ModaNet
  • Semantic segmentation
  • Street-fashion
  • U-Net

ASJC Scopus subject areas

  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Dang, A. H., & Kameyama, W. (2019). Semantic segmentation of fashion photos using light-weight asymmetric U-Net. In 2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019 (pp. 175-178). [9015571] (2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE46687.2019.9015571