AAAI Publications, Twenty-Second International Joint Conference on Artificial Intelligence

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Wsabie: Scaling Up to Large Vocabulary Image Annotation
Jason Weston, Samy Bengio, Nicolas Usunier

Last modified: 2011-06-29

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 low-dimensional joint embedding space for both images and annotations. Our method, called Wsabie, both outperforms several baseline methods and is faster and consumes less memory.

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