Unsupervised learning of discriminative attributes and visual representations
Refereed conference paper presented and published in conference proceedings

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AbstractAttributes offer useful mid-level features to interpret visual data. While most attribute learning methods are supervised by costly human-generated labels, we introduce a simple yet powerful unsupervised approach to learn and predict visual attributes directly from data. Given a large unlabeled image collection as input, we train deep Convolutional Neural Networks (CNNs) to output a set of discriminative, binary attributes often with semantic meanings. Specifically, we first train a CNN coupled with unsupervised discriminative clustering, and then use the cluster membership as a soft supervision to discover shared attributes from the clusters while maximizing their separability. The learned attributes are shown to be capable of encoding rich imagery properties from both natural images and contour patches. The visual representations learned in this way are also transferrable to other tasks such as object detection. We show other convincing results on the related tasks of image retrieval and classification, and contour detection.
All Author(s) ListHuang C., Lo C.C., Tang X.
Name of Conference2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
Start Date of Conference26/06/2016
End Date of Conference01/07/2016
Place of ConferenceLas Vegas
Country/Region of ConferenceUnited States of America
Volume Number2016-January
Pages5175 - 5184
LanguagesEnglish-United Kingdom

Last updated on 2020-08-07 at 03:25