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Learning Deep Structure-Preserving Image-Text Embeddings
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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 textto-image retrieval. Our method achieves new state-of-theart 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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Metadata
- Authors
- Wang, L., Li, Y., & Lazebnik, S.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 5005-5013
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963389687
- Cited by
- 760
- References
- 56
- FWCI
- 32.89
- Type
- conference paper
- Language
- en
- Open access
- green
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