Learning Deep Structure Preserving Image Text Embeddings
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.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)