Papers › Learning Deep Structure-Preserving Image-Text Embeddings
Learning Deep Structure-Preserving Image-Text Embeddings
Liwei Wang, Yin Li, Svetlana Lazebnik
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | Flickr30K 1K test | SPE | R@1 | 29.7 | #15 of 18 | Archive leaderboard | report |
| Image Retrieval | Flickr30K 1K test | SPE | R@10 | 72.1 | #15 of 18 | Archive leaderboard | report |
| Image Retrieval | Flickr30K 1K test | SPE | R@5 | 60.1 | #15 of 18 | Archive leaderboard | report |
| Phrase Grounding | Flickr30k Entities Test | DSPE | R@1 | 43.89 | #14 of 18 | Archive leaderboard | report |
| Phrase Grounding | Flickr30k Entities Test | DSPE | R@10 | 68.66 | #14 of 18 | Archive leaderboard | report |
| Phrase Grounding | Flickr30k Entities Test | DSPE | R@5 | 64.46 | #14 of 18 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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