Learning to compose visual relationships from raw images in the form of scene graphs is a highly challenging task due to contextual dependencies, but it is essential in computer vision applications that depend on scene understanding. However, no current approaches in Scene Graph Generation (SGG) aim at providing useful graphs for downstream tasks. Instead, the main focus has primarily been on the task of unbiasing the data distribution for predicting more fine-grained relations. That being said, all fine-grained relations are not equally relevant and at least a part of them are of no use for real-world applications. In this work, we introduce the task of Efficient SGG that prioritizes the generation of relevant relations, facilitating the use of Scene Graphs in downstream tasks such as Image Generation. To support further approaches in this task, we present a new dataset, VG150-curated, based on the annotations of the popular Visual Genome dataset. We show through a set of experiments that this dataset contains more high-quality and diverse annotations than the one usually adopted by approaches in SGG. Finally, we show the efficiency of this dataset in the task of Image Generation from Scene Graphs. Our approach can be easily replicated to improve the quality of other Scene Graph Generation datasets.

本文提出一项名为“Efficient SGG”的任务，旨在优先生成相关关系以及提供一个新的数据集VG150，以支持在图像生成等下游任务中利用场景图。通过一系列实验，我们证明VG150数据集比通常在场景图生成方法中采用的数据集包含更高质量和多样性的注释。

粒度太粗糙：一种新的数据中心方法，用于高效生成场景图