TY - GEN
T1 - DETECTION OF FASHION LANDMARKS BASED ON POSE ESTIMATION AND HUMAN PARSING
AU - He, Honghong
AU - Zhou, Yanghong
AU - Fan, Jin Tu
AU - Mok, P. Y.
N1 - Funding Information:
This research is funded by the Laboratory for Artificial Intelligence in Design (Project Code: RP1-1) under the InnoHK Research Clusters, Hong Kong Special Administrative Region Government.
Publisher Copyright:
© MCCSIS 2022.All rights reserved.
PY - 2022
Y1 - 2022
N2 - Locating fashion items on any input images is referred as 'fashion image understanding', which is often a preliminary or initial step that supports various other visions tasks. The study of fashion image understanding has been widely benefited from the rapid advancements in deep learning-based new models and the availability of large datasets covering real-world fashion images. However, the real value of these datasets is in doubt, because different annotation strategies have been adopted in these datasets, resulting in different landmarks for different clothing styles and low compatibility of these datasets. In this paper, we propose a pose-aware segmentation-based method to locate key points of fashion items on fashion images by taking advantage of clear correspondence between clothing and human body, that can be applied to locate key points of fashion items and to re-annotate existing datasets for cross dataset learning. The validity of the method was validated on a subset of the DeepFashion2 dataset.
AB - Locating fashion items on any input images is referred as 'fashion image understanding', which is often a preliminary or initial step that supports various other visions tasks. The study of fashion image understanding has been widely benefited from the rapid advancements in deep learning-based new models and the availability of large datasets covering real-world fashion images. However, the real value of these datasets is in doubt, because different annotation strategies have been adopted in these datasets, resulting in different landmarks for different clothing styles and low compatibility of these datasets. In this paper, we propose a pose-aware segmentation-based method to locate key points of fashion items on fashion images by taking advantage of clear correspondence between clothing and human body, that can be applied to locate key points of fashion items and to re-annotate existing datasets for cross dataset learning. The validity of the method was validated on a subset of the DeepFashion2 dataset.
KW - Fashion Landmarks Localization
KW - Graph-based Clothing Structure
KW - Human Parsing
KW - Pose Estimation
UR - http://www.scopus.com/inward/record.url?scp=85142389767&partnerID=8YFLogxK
M3 - Conference article published in proceeding or book
AN - SCOPUS:85142389767
T3 - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
SP - 62
EP - 69
BT - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
PB - IADIS Press
T2 - 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing, CGVCVIP 2022, 8th International Conference on Connected Smart Cities, CSC 2022, 7th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th International Conference on Theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi Conference on Computer Science and Information Systems, MCCSIS 2022
Y2 - 19 July 2022 through 22 July 2022
ER -