TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Image Retrieval	Flickr30K 1K test	SPE	R@1	29.7	# 15
Image Retrieval	Flickr30K 1K test	SPE	R@10	72.1	# 14
Image Retrieval	Flickr30K 1K test	SPE	R@5	60.1	# 13

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Learning Deep Structure-Preserving Image-Text Embeddings

CVPR 2016 · 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.