  Despite progress in perceptual tasks such as image classification, computers
still perform poorly on cognitive tasks such as image description and question
answering. Cognition is core to tasks that involve not just recognizing, but
reasoning about our visual world. However, models used to tackle the rich
content in images for cognitive tasks are still being trained using the same
datasets designed for perceptual tasks. To achieve success at cognitive tasks,
models need to understand the interactions and relationships between objects in
an image. When asked "What vehicle is the person riding?", computers will need
to identify the objects in an image as well as the relationships riding(man,
carriage) and pulling(horse, carriage) in order to answer correctly that "the
person is riding a horse-drawn carriage".
  In this paper, we present the Visual Genome dataset to enable the modeling of
such relationships. We collect dense annotations of objects, attributes, and
relationships within each image to learn these models. Specifically, our
dataset contains over 100K images where each image has an average of 21
objects, 18 attributes, and 18 pairwise relationships between objects. We
canonicalize the objects, attributes, relationships, and noun phrases in region
descriptions and questions answer pairs to WordNet synsets. Together, these
annotations represent the densest and largest dataset of image descriptions,
objects, attributes, relationships, and question answers.
