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Learning Deep Structure-Preserving Image-Text Embeddings
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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 largemargin 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
doi:10.1109/cvpr.2016.541
dblp:conf/cvpr/WangLL16
fatcat:ximgbdttdrc7xfqe4jf4qfin6u