About This Document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Zeynep Akata
- sl:arxiv_num : 1503.08677
- sl:arxiv_published : 2015-03-30T14:04:34Z
- sl:arxiv_summary : Attributes act as intermediate representations that enable parameter sharing
between classes, a must when training data is scarce. We propose to view
attribute-based image classification as a label-embedding problem: each class
is embedded in the space of attribute vectors. We introduce a function that
measures the compatibility between an image and a label embedding. The
parameters of this function are learned on a training set of labeled samples to
ensure that, given an image, the correct classes rank higher than the incorrect
ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets
show that the proposed framework outperforms the standard Direct Attribute
Prediction baseline in a zero-shot learning scenario. Label embedding enjoys a
built-in ability to leverage alternative sources of information instead of or
in addition to attributes, such as e.g. class hierarchies or textual
descriptions. Moreover, label embedding encompasses the whole range of learning
settings from zero-shot learning to regular learning with a large number of
labeled examples.@en
- sl:arxiv_title : Label-Embedding for Image Classification@en
- sl:arxiv_updated : 2015-10-01T10:48:38Z
- sl:bookmarkOf : https://arxiv.org/abs/1503.08677
- sl:creationDate : 2020-02-18
- sl:creationTime : 2020-02-18T15:00:20Z
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