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