  Dense captioning is a newly emerging computer vision topic for understanding
images with dense language descriptions. The goal is to densely detect visual
concepts (e.g., objects, object parts, and interactions between them) from
images, labeling each with a short descriptive phrase. We identify two key
challenges of dense captioning that need to be properly addressed when tackling
the problem. First, dense visual concept annotations in each image are
associated with highly overlapping target regions, making accurate localization
of each visual concept challenging. Second, the large amount of visual concepts
makes it hard to recognize each of them by appearance alone. We propose a new
model pipeline based on two novel ideas, joint inference and context fusion, to
alleviate these two challenges. We design our model architecture in a
methodical manner and thoroughly evaluate the variations in architecture. Our
final model, compact and efficient, achieves state-of-the-art accuracy on
Visual Genome for dense captioning with a relative gain of 73\% compared to the
previous best algorithm. Qualitative experiments also reveal the semantic
capabilities of our model in dense captioning.
