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Panoptic Scene Graph generation (PSG) is a recently proposed task in imagescene understanding that aims to segment the image and extract triplets ofsubjects, objects and their relations to build a scene graph. This task isparticularly challenging for two reasons. First, it suffers from a long-tailproblem in its relation categories, making naive biased methods more inclinedto high-frequency relations. Existing unbiased methods tackle the long-tailproblem by data/loss rebalancing to favor low-frequency relations. Second, asubject-object pair can have two or more semantically overlapping relations.While existing methods favor one over the other, our proposed HiLo frameworklets different network branches specialize on low and high frequency relations,enforce their consistency and fuse the results. To the best of our knowledge weare the first to propose an explicitly unbiased PSG method. In extensiveexperiments we show that our HiLo framework achieves state-of-the-art resultson the PSG task. We also apply our method to the Scene Graph Generation taskthat predicts boxes instead of masks and see improvements over all baselinemethods. Code is available at https://github.com/franciszzj/HiLo.

HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation