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Our analysis shows that\nobject labels are highly predictive of relation labels but not vice-versa. We\nalso find that there are recurring patterns even in larger subgraphs: more than\n50% of graphs contain motifs involving at least two relations. Our analysis\nmotivates a new baseline: given object detections, predict the most frequent\nrelation between object pairs with the given labels, as seen in the training\nset. This baseline improves on the previous state-of-the-art by an average of\n3.6% relative improvement across evaluation settings. We then introduce Stacked\nMotif Networks, a new architecture designed to capture higher order motifs in\nscene graphs that further improves over our strong baseline by an average 7.1%\nrelative gain. 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