We present a model that generates natural language de scriptions of images and their regions. Our approach lever ages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between lan guage and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architec ture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in re trieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions sig nificantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations.
REF adopts a model that can learn the correspondence between short phrases within sentences and image region.
Deep visual-semantic alignments for generating image descriptions
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science Medicine