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, 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 use in SGG. Finally, we show the efficiency of this dataset in the task of Image Generation from Scene Graphs.

翻译：从原始图像中以场景图的形式学习组合视觉关系是一项极具挑战性的任务，因为它依赖于上下文依赖关系，但这对于依赖场景理解的计算机视觉应用至关重要。然而，目前场景图生成领域的方法并非旨在为下游任务提供有用的图结构。相反，主要焦点一直集中在通过去偏数据分布来预测更细粒度的关系上。即便如此，所有细粒度关系的相关性并不相同，其中至少有一部分对实际应用毫无用处。在这项工作中，我们引入了高效场景图生成任务，该任务优先生成相关关系，从而促进场景图在下游任务（如图像生成）中的应用。为了支持进一步的研究，我们基于流行的视觉基因组数据集的标注，提出了一个新的数据集VG150-curated。通过一系列实验，我们证明该数据集包含比SGG中通常使用的数据集更高质量和更多样化的标注。最后，我们展示了该数据集在从场景图生成图像任务中的有效性。