Panoptic Scene Graph generation (PSG) is a recently proposed task in image scene understanding that aims to segment the image and extract triplets of subjects, objects and their relations to build a scene graph. This task is particularly challenging for two reasons. First, it suffers from a long-tail problem in its relation categories, making naive biased methods more inclined to high-frequency relations. Existing unbiased methods tackle the long-tail problem by data/loss rebalancing to favor low-frequency relations. Second, a subject-object pair can have two or more semantically overlapping relations. While existing methods favor one over the other, our proposed HiLo framework lets different network branches specialize on low and high frequency relations, enforce their consistency and fuse the results. To the best of our knowledge we are the first to propose an explicitly unbiased PSG method. In extensive experiments we show that our HiLo framework achieves state-of-the-art results on the PSG task. We also apply our method to the Scene Graph Generation task that predicts boxes instead of masks and see improvements over all baseline methods.

提出了一种名为Panoptic Scene Graph generation的任务，该任务旨在分段图像并提取三元组，同时利用HiLo框架解决长尾问题和语义重叠问题，以实现无偏的PSG方法。在广泛的实验中取得了最新的实验结果，并应用于预测场景图中的盒子的场景图生成任务中，相比所有基线方法均有所改善。

HiLo: 利用高低频率关系进行无偏差全景场景图生成