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We study scalable and uniform understanding of facts in images. Existingvisual recognition systems are typically modeled differently for each fact typesuch as objects, actions, and interactions. We propose a setting where allthese facts can be modeled simultaneously with a capacity to understandunbounded number of facts in a structured way. The training data comes asstructured facts in images, including (1) objects (e.g., $<$boy$>$), (2)attributes (e.g., $<$boy, tall$>$), (3) actions (e.g., $<$boy, playing$>$), and(4) interactions (e.g., $<$boy, riding, a horse $>$). Each fact has a semanticlanguage view (e.g., $<$ boy, playing$>$) and a visual view (an image with thisfact). We show that learning visual facts in a structured way enables not onlya uniform but also generalizable visual understanding. We propose andinvestigate recent and strong approaches from the multiview learning literatureand also introduce two learning representation models as potential baselines.We applied the investigated methods on several datasets that we augmented withstructured facts and a large scale dataset of more than 202,000 facts and814,000 images. Our experiments show the advantage of relating facts by thestructure by the proposed models compared to the designed baselines onbidirectional fact retrieval.

Sherlock: Scalable Fact Learning in Images