Learning Deep Structure-Preserving Image-Text Embeddings

Authors:
  • LWLiwei Wang
    Liwei Wang
  • YLYin Li
    Yin Li
  • SLSvetlana Lazebnik
    Svetlana Lazebnik
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Abstract
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 text-to-image retrieval. Our method achieves new state-of-the-art results on the Flickr30K and MSCOCO image-sentence datasets and shows promise on the new task of phrase localization on the Flickr30K Entities dataset.

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