Article

Describing clothing by semantic attributes

Authors:

Andrew Gallagher,

Bernd GirodAuthors Info & Claims

ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part III

Pages 609 - 623

https://doi.org/10.1007/978-3-642-33712-3_44

Published: 07 October 2012 Publication History

Abstract

Describing clothing appearance with semantic attributes is an appealing technique for many important applications. In this paper, we propose a fully automated system that is capable of generating a list of nameable attributes for clothes on human body in unconstrained images. We extract low-level features in a pose-adaptive manner, and combine complementary features for learning attribute classifiers. Mutual dependencies between the attributes are then explored by a Conditional Random Field to further improve the predictions from independent classifiers. We validate the performance of our system on a challenging clothing attribute dataset, and introduce a novel application of dressing style analysis that utilizes the semantic attributes produced by our system.

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Cited By

Yuan YZhang WDeng YLam W(2023)Multimodal Fashion Knowledge Extraction as CaptioningProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625315(52-62)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625315
Deldjoo YNazary FRamisa AMcAuley JPellegrini GBellogin ANoia T(2023)A Review of Modern Fashion Recommender SystemsACM Computing Surveys10.1145/362473356:4(1-37)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3624733
Song MZhang LYuan MLi ZSong QLiu YZheng G(2023)CoTel: Ontology-Neural Co-Enhanced Text LabelingProceedings of the ACM Web Conference 202310.1145/3543507.3583533(1897-1906)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583533
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Describing clothing by semantic attributes
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning algorithms

Recommendations

Watch fashion shows to tell clothing attributes
Highlights
- Collect two new clothing video datasets and two new clothing attribute image datasets.
- A novel semi-supervised approach is proposed for clothing attribute learning.
- Unsupervised triplet network is trained with unlabeled video ...
Abstract
In this paper, we propose a novel semi-supervised method to predict clothing attributes with the assistance of unlabeled data like fashion shows. To this end, a two-stage framework is built, i.e., the unsupervised triplet network pre-training ...
Clothing Attributes Assisted Person Reidentification
Person reidentification across nonoverlapping camera views is a rather challenging task. Due to the difficulties in obtaining identifiable faces, clothing appearance becomes the main cue for identification purposes. In this paper, we present a ...
Software describing attributes

This article represents a step towards defining attributes of software and the metrics of these attributes by creating a framework of them. By standardization both, the attributes and the metrics, the description of software makes useful in its life ...

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Information & Contributors

Information

Published In

cover image Guide Proceedings

ECCV'12: Proceedings of the 12th European conference on Computer Vision - Volume Part III

October 2012

880 pages

ISBN:9783642337116

Editors:
Andrew Fitzgibbon
Microsoft Research Ltd., Cambridge, UK
,
Svetlana Lazebnik
Dept. of Computer Science, University of North Carolina, Chapel Hill, NC, UK
,
Pietro Perona
Dept. of Computer Science, California Institute of Technology, Pasadena, CA, UK
,
Yoichi Sato
Institute of Industrial Science, The University of Tokyo, Pasadena, Tokyo, Japan
,
Cordelia Schmid
Institute of Industrial Science, INRIA, Montbonnot, Tokyo, France

Sponsors

TOYOTA: TOYOTA
Google Inc.
IBMR: IBM Research
NVIDIA
Microsoft Reasearch: Microsoft Reasearch

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 07 October 2012

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Cited By

Yuan YZhang WDeng YLam W(2023)Multimodal Fashion Knowledge Extraction as CaptioningProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625315(52-62)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625315
Deldjoo YNazary FRamisa AMcAuley JPellegrini GBellogin ANoia T(2023)A Review of Modern Fashion Recommender SystemsACM Computing Surveys10.1145/362473356:4(1-37)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3624733
Song MZhang LYuan MLi ZSong QLiu YZheng G(2023)CoTel: Ontology-Neural Co-Enhanced Text LabelingProceedings of the ACM Web Conference 202310.1145/3543507.3583533(1897-1906)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583533
Zhang XShen MLi XWang X(2023)AABLSTM: A Novel Multi-task Based CNN-RNN Deep Model for Fashion AnalysisACM Transactions on Multimedia Computing, Communications, and Applications10.1145/351902919:1(1-18)Online publication date: 5-Jan-2023
https://dl.acm.org/doi/10.1145/3519029
Wang THuang YQian J(2023)Fashion Label Relation Networks for Attribute RecognitionArtificial Intelligence10.1007/978-981-99-8850-1_28(340-351)Online publication date: 22-Jul-2023
https://dl.acm.org/doi/10.1007/978-981-99-8850-1_28
Zhao YLi APeng GWang Y(2023)Transpose and Mask: Simple and Effective Logit-Based Knowledge Distillation for Multi-attribute and Multi-label ClassificationPattern Recognition and Computer Vision10.1007/978-981-99-8549-4_23(273-284)Online publication date: 13-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-99-8549-4_23
Greco AVento B(2023)PAR Contest 2023: Pedestrian Attributes Recognition with Multi-task LearningComputer Analysis of Images and Patterns10.1007/978-3-031-44237-7_1(3-12)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-44237-7_1
Peng DLiu RLu JZhang S(2022)Unsupervised multi-modal modeling of fashion styles with visual attributesApplied Soft Computing10.1016/j.asoc.2021.108214115:COnline publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.asoc.2021.108214
Cestari IPortinale LRiva P(2022)A Hybrid Recommender System with Implicit Feedbacks in Fashion RetailAIxIA 2022 – Advances in Artificial Intelligence10.1007/978-3-031-27181-6_15(212-224)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-27181-6_15
Kamath AClark CGupta TKolve EHoiem DKembhavi A(2022)Webly Supervised Concept Expansion for General Purpose Vision ModelsComputer Vision – ECCV 202210.1007/978-3-031-20059-5_38(662-681)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20059-5_38
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