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Conference Paper: Fashion landmark detection in the wild

TitleFashion landmark detection in the wild
Authors
KeywordsAttribute prediction
Clothes landmark detection
Clothes retrieval
Cascaded deep convolutional neural networks
Issue Date2016
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, v. 9906 LNCS, p. 229-245 How to Cite?
Abstract© Springer International Publishing AG 2016. 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 (The dataset is available at http://mmlab.ie.cuhk.edu. hk/projects/DeepFashion/LandmarkDetection.html.) 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.
Persistent Identifierhttp://hdl.handle.net/10722/273574
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorLiu, Ziwei-
dc.contributor.authorYan, Sijie-
dc.contributor.authorLuo, Ping-
dc.contributor.authorWang, Xiaogang-
dc.contributor.authorTang, Xiaoou-
dc.date.accessioned2019-08-12T09:55:58Z-
dc.date.available2019-08-12T09:55:58Z-
dc.date.issued2016-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, v. 9906 LNCS, p. 229-245-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/273574-
dc.description.abstract© Springer International Publishing AG 2016. 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 (The dataset is available at http://mmlab.ie.cuhk.edu. hk/projects/DeepFashion/LandmarkDetection.html.) 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.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectAttribute prediction-
dc.subjectClothes landmark detection-
dc.subjectClothes retrieval-
dc.subjectCascaded deep convolutional neural networks-
dc.titleFashion landmark detection in the wild-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-46475-6_15-
dc.identifier.scopuseid_2-s2.0-84990848464-
dc.identifier.volume9906 LNCS-
dc.identifier.spage229-
dc.identifier.epage245-
dc.identifier.eissn1611-3349-

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