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- Publisher Website: 10.1007/978-3-319-46475-6_15
- Scopus: eid_2-s2.0-84990848464
- WOS: WOS:000389383900015
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Conference Paper: Fashion landmark detection in the wild
Title | Fashion landmark detection in the wild |
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Authors | |
Keywords | Attribute prediction Clothes landmark detection Clothes retrieval Cascaded deep convolutional neural networks |
Issue Date | 2016 |
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 Identifier | http://hdl.handle.net/10722/273574 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, Ziwei | - |
dc.contributor.author | Yan, Sijie | - |
dc.contributor.author | Luo, Ping | - |
dc.contributor.author | Wang, Xiaogang | - |
dc.contributor.author | Tang, Xiaoou | - |
dc.date.accessioned | 2019-08-12T09:55:58Z | - |
dc.date.available | 2019-08-12T09:55:58Z | - |
dc.date.issued | 2016 | - |
dc.identifier.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 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://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.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Attribute prediction | - |
dc.subject | Clothes landmark detection | - |
dc.subject | Clothes retrieval | - |
dc.subject | Cascaded deep convolutional neural networks | - |
dc.title | Fashion landmark detection in the wild | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-319-46475-6_15 | - |
dc.identifier.scopus | eid_2-s2.0-84990848464 | - |
dc.identifier.volume | 9906 LNCS | - |
dc.identifier.spage | 229 | - |
dc.identifier.epage | 245 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.isi | WOS:000389383900015 | - |
dc.identifier.issnl | 0302-9743 | - |