N.: Wsabie: Scaling up to large vocabulary image annotation
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by
Jason Weston
,
Samy Bengio
,
Nicolas Usunier
| Venue: | In: IJCAI |
| Citations: | 84 - 11 self |
BibTeX
@ARTICLE{Weston_n.:wsabie:,
author = {Jason Weston and Samy Bengio and Nicolas Usunier},
title = {N.: Wsabie: Scaling up to large vocabulary image annotation},
journal = {In: IJCAI},
year = {},
pages = {2764--2770}
}
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Abstract
Image annotation datasets are becoming larger and larger, with tens of millions of images and tens of thousands of possible annotations. We propose a strongly performing method that scales to such datasets by simultaneously learning to optimize precision at the top of the ranked list of annotations for a given image and learning a lowdimensional joint embedding space for both images and annotations. Our method, called WSABIE, both outperforms several baseline methods and is faster and consumes less memory. 1
