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Dense captioning is a newly emerging computer vision topic for understandingimages with dense language descriptions. The goal is to densely detect visualconcepts (e.g., objects, object parts, and interactions between them) fromimages, labeling each with a short descriptive phrase. We identify two keychallenges of dense captioning that need to be properly addressed when tacklingthe problem. First, dense visual concept annotations in each image areassociated with highly overlapping target regions, making accurate localizationof each visual concept challenging. Second, the large amount of visual conceptsmakes it hard to recognize each of them by appearance alone. We propose a newmodel pipeline based on two novel ideas, joint inference and context fusion, toalleviate these two challenges. We design our model architecture in amethodical manner and thoroughly evaluate the variations in architecture. Ourfinal model, compact and efficient, achieves state-of-the-art accuracy onVisual Genome for dense captioning with a relative gain of 73\% compared to theprevious best algorithm. Qualitative experiments also reveal the semanticcapabilities of our model in dense captioning.

Dense Captioning with Joint Inference and Visual Context