Attention to describe products with attributes

Shuohao Li, Kota Yamaguchi, Takayuki Okatani

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

1 Citation (Scopus)

Abstract

In e-commerce environment, shop owners and advertisers give descriptive details of the product to attract potential customers. Can a computer vision technique recognize and describe the details of a product in the same way? In this paper, we study how the attention mechanism benefits in product phrase generation with attributes. We present a phrase generation model consisting of convolutional neural networks, recurrent neural networks, and the attention mechanism to look into the detail of the image. We construct attribute-rich phrases from metadata in Easy dataset that consist of an adjective, a material tag, and product category, and learn the model to describe products. Our empirical results suggest that our model improves the description quality in both machine-translation metric and human evaluation.

Original languageEnglish
Title of host publicationProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages215-218
Number of pages4
ISBN (Electronic)9784901122160
DOIs
Publication statusPublished - 2017 Jul 19
Event15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
Duration: 2017 May 82017 May 12

Publication series

NameProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

Other

Other15th IAPR International Conference on Machine Vision Applications, MVA 2017
CountryJapan
CityNagoya
Period17/5/817/5/12

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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