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Learning Deep Structure-Preserving Image-Text Embeddings
[article]
2016
arXiv
pre-print
This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities. The network is trained using a large margin objective that combines cross-view ranking constraints with within-view neighborhood structure preservation constraints inspired by metric learning literature. Extensive experiments show that our approach gains significant improvements in accuracy for image-to-text and
arXiv:1511.06078v2
fatcat:zagdm4qg3resxfgnv4afq4xyvm