Fashion Landmark Detection in the Wild
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Visual fashion analysis has attracted many attentions in the
recent years. Previous work represented clothing regions by either bounding
boxes or human joints. This work presents fashion landmark detection
or fashion alignment, which is to predict the positions of functional key
points defined on the fashion items, such as the corners of neckline,
hemline, and cuff. To encourage future studies, we introduce a fashion
landmark dataset with over 120K images, where each image is labeled
with eight landmarks. With this dataset, we study fashion alignment
by cascading multiple convolutional neural networks in three stages.
These stages gradually improve the accuracies of landmark predictions.
Extensive experiments demonstrate the effectiveness of the proposed
method, as well as its generalization ability to pose estimation. Fashion
landmark is also compared to clothing bounding boxes and human joints
in two applications, fashion attribute prediction and clothes retrieval,
showing that fashion landmark is a more discriminative representation
to understand fashion images.

Acceptance Date11/07/2016
All Author(s) ListZiwei Liu, Sijie Yan, Ping Luo, Xiaogang Wang, Xiaoou Tang
Name of ConferenceThe 14th European Conference on Computer Vision
Start Date of Conference08/10/2016
End Date of Conference16/10/2016
Place of ConferenceAmsterdam
Country/Region of ConferenceNetherlands
Journal nameLecture Notes in Artificial Intelligence
Proceedings TitleComputer Vision – ECCV 2016
Series TitleLecture Notes in Computer Science
Number in Series9906
Volume Number9906
PublisherSpringer International Publishing
Pages229 - 245
LanguagesEnglish-United States

Last updated on 2021-24-09 at 23:44