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Evaluation of Output Embeddings for Fine-Grained Image Classification Zeynep Akata∗ , Scott Reed† , Daniel Walter† , Honglak Lee† and Bernt Schiele∗ ∗ Computer Vision and Multimodal Computing Max Planck Insti
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Document Date: 2015-07-10 12:22:22

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City

Saarbrucken / /

Company

Convolutional Neural Networks / Google / /

Country

Germany / /

Event

Extinction / /

Facility

Engineering Division University of Michigan / Multimodal Computing Max Planck Institute / W W building / /

IndustryTerm

grid search / neural network / cleanly pre-processing / computer vision algorithms / neural networks / dot product / unlabeled online textual resources / similarity search / taskspecific pre-processing / /

MusicGroup

Ours / /

Organization

University of Michigan / Ann Arbor / National Science Foundation / Computer Science and Engineering Division University / Computer Vision and Multimodal Computing Max Planck Institute for Informatics / U.S. Securities and Exchange Commission / Stanford / UCSD / /

Person

N. Usunier / S. Bengio / Daniel Walter / Scott Reed / D. Parikh / J. Dean / D. J. Crandall / J. Shlens / K. Grauman / T. Mikolov / J. Weston / K. Chen / D. Erhan / V / G. Corrado / I. Sutskever / Y. Singer / Stanford Dogs (Dogs) / K. Duan / G. Kulkarni / V / G. S. Corrado / A. Frome / /

Position

Fisher / Singer / /

Product

WordNet / Word2Vec / Fisher Vectors / /

ProvinceOrState

Michigan / /

PublishedMedium

Computer Graphics / /

Technology

computer vision algorithms / neural network / ALE / /

URL

http /

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