Multi-depth dilated network for fashion landmark detection

Zeng Kai, Jun Feng, Richard Sutcliffe, Wang Xiaoyu, Bu Qirong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The topic of fashion landmark detection has become popular recently, especially with the development of convolutional neural networks. However, there are still some challenges that need to be addressed, including the detection of hard keypoints such as those which are occluded or invisible. To tackle this problem, we propose in this paper a novel Multi-Depth Dilated (MDD) block which is composed of different numbers of dilated convolutions in parallel, so that the MDD can effectively extract large-scale context information at different levels and hence obtain relative positional relationship information between keypoints, information which is necessary for the inference of hard keypoints. Furthermore, we use an Online Hard Keypoints Mining method for training to further boost the effectiveness of hard keypoints detection. Through the experiment, we demonstrate that by stacking the MDD blocks, we construct a Multi-Depth Dilated Network (MDDNet) that achieves state-of-the-art results on fashion benchmark datasets.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages60-65
Number of pages6
ISBN (Electronic)9781538692141
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes
Event2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019 - Shanghai, China
Duration: 8 Jul 201912 Jul 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019

Conference

Conference2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019
Country/TerritoryChina
CityShanghai
Period8/07/1912/07/19

Keywords

  • Convolutional Neural Network
  • Deep Learning
  • Fashion Landmark Detection

Fingerprint

Dive into the research topics of 'Multi-depth dilated network for fashion landmark detection'. Together they form a unique fingerprint.

Cite this