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SisterNetwork: Enhancing Robustness of Multi-label Classification with Semantically Segmented Images

  • Holim LimEmail author
  • Jeeseung HanEmail author
  • Sang-goo Lee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 935)

Abstract

The rapid growth of the online fashion market has raised the demand for fashion technologies, such as clothing attribute tagging. However, handling fashion image data is challenging since fashion images likely contain irrelevant backgrounds and involve various deformations. In this paper, we introduce SisterNetwork, a deep learning model to tackle the multi-label classification task for fashion attribute tagging. The proposed model consists of two different CNNs to leverage both the original image and the semantic segmentation information. We evaluate our model on the DCSA dataset which contains tagged fashion images, and we achieved the state-of-the-art performance on the multi-label classification task.

Keywords

Multi-label classification Semantic segmentation Fashion 

Notes

Acknowledgments

This work was supported by the Technology development Program (S2646078) funded by the Ministry of SMEs and Startups (MSS, Korea).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IntelliSys Corp.Seoul National UniversitySeoulSouth Korea

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