The goal of this paper is to detect objects by exploiting their interrelationships. Rather than relying on predefined and labeled graph structures, we infer a graph prior from object co-occurrence statistics. The key idea of our paper is to model object relations as a function of initial class predictions and co-occurrence priors to generate a graph representation of an image for improved classification and bounding box regression. We additionally learn the object-relation joint distribution via energy based modeling. Sampling from this distribution generates a refined graph representation of the image which in turn produces improved detection performance. Experiments on the Visual Genome and MS-COCO datasets demonstrate our method is detector agnostic, end-to-end trainable, and especially beneficial for rare object classes. What is more, we establish a consistent improvement over object detectors like DETR and Faster-RCNN, as well as state-of-the-art methods modeling object interrelationships.

翻译：本文的目的是通过利用天体的相互关系来探测天体。 我们不是依靠预先定义和标记的图形结构,而是先从天体共同发生统计中推断出一个图表。 我们的论文的关键思想是将天体关系模型作为初始类预测和共同发生前的函数,以生成图像的图形表示方式来改进分类和捆绑框回归。 我们还通过基于能源的模型来学习天体- 关系联合分布。 从此分布中取样生成图像的精细图形表示方式,从而产生更好的探测性能。 对视觉基因组和 MS- CO 数据集的实验表明我们的方法是检测性、 终端到终端的、 特别有利于稀有天体类别的。 更重要的是, 我们对像 DETR 和 Fear- RCNN 这样的天体探测器以及最先进的模型对象相互关系进行一致的改进。