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Scene graph generation (SGG) aims to capture a wide variety of interactionsbetween pairs of objects, which is essential for full scene understanding.Existing SGG methods trained on the entire set of relations fail to acquirecomplex reasoning about visual and textual correlations due to various biasesin training data. Learning on trivial relations that indicate generic spatialconfiguration like 'on' instead of informative relations such as 'parked on'does not enforce this complex reasoning, harming generalization. To addressthis problem, we propose a novel framework for SGG training that exploitsrelation labels based on their informativeness. Our model-agnostic trainingprocedure imputes missing informative relations for less informative samples inthe training data and trains a SGG model on the imputed labels along withexisting annotations. We show that this approach can successfully be used inconjunction with state-of-the-art SGG methods and improves their performancesignificantly in multiple metrics on the standard Visual Genome benchmark.Furthermore, we obtain considerable improvements for unseen triplets in a morechallenging zero-shot setting.

Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation