Deep Visual-Semantic Alignments for Generating Image Descriptions

We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks (RNN) over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state…

• NS
  National Science Foundation

  Award: ISS-1115313
• N
  Nvidia
• MU
  Multidisciplinary University Research Initiative
• OO
  Office of Naval Research