Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,803

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations

Liu, Ziwei, Luo, Ping, Qiu, Shi, Wang, Xiaogang, Tang, Xiaoou·2016·2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2016.124
Open this paper in CoChat — free account

Abstract

Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion1, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/cvpr.2016.124

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 28

  1. 10.1109/CVPR.2014.407 · DOI
  2. 10.1109/CVPR.2012.6248101 · DOI · Paper page
  3. 10.1109/ICCV.2013.437 · DOI · Paper page
  4. 10.1145/2647868.2654958 · DOI
  5. 10.1109/CVPR.2011.5995741 · DOI · Paper page
  6. 10.1109/ICCV.2015.127 · DOI · Paper page
  7. 10.1145/2461466.2461485 · DOI · Paper page
  8. 10.1109/ICCV.2015.382 · DOI · Paper page
  9. kiapour, 2014, Hipster wars: Discovering elements of fashion styles, ECCV, 472
  10. 10.1109/TMM.2016.2542983 · DOI · Paper page
  11. 10.1109/TMM.2013.2285526 · DOI · Paper page
  12. 10.1145/2393347.2396471 · DOI · Paper page
  13. 10.1109/ICCV.2015.162 · DOI · Paper page
  14. 10.1145/2072298.2072013 · DOI
  15. 10.1109/CVPR.2015.7299169 · DOI · Paper page
  16. 10.1109/ICCV.2015.527 · DOI · Paper page
  17. chen, 2012, Describing clothing by semantic attributes, ECCV, 609
  18. 10.1109/CVPRW.2013.6 · DOI
  19. xiao, 2015, Learning from massive noisy labeled data for image classification, CVPR, 2691
  20. fu, 2012, Efficient clothing retrieval with semantic-preserving visual phrases, ACCV, 420
  21. 10.1109/ICCV.2009.5459303 · DOI · Paper page
  22. gong, 2013, Deep Convolutional Ranking for Multilabel Image Annotation
  23. bossard, 2012, Apparel classification with style, ACCV, 321
  24. 10.1109/ICCV.2013.329 · DOI
  25. simo-serra, 2015, Neuroaesthetics in fashion: Modeling the perception of beauty, CVPR
  26. simo-serra, 2014, A high performance crf model for clothes parsing, ACCV
  27. 10.1109/CVPR.2014.214 · DOI · Paper page
  28. simonyan, 2014, Very Deep Convolutional Networks for Large-scale Image Recognition

Cited by

See all 1,803

Metadata

Authors
Liu, Z., Luo, P., Qiu, S., Wang, X., & Tang, X.
Venue
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2016-06
Pages
1096-1104
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2016.124
OpenAlex
W2471768434
Cited by
1,803
References
7
FWCI
48.87
Type
conference paper
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.