IEEE Trans Pattern Anal Mach Intell. 2023 Aug;45(8):10161-10172. doi: 10.1109/TPAMI.2023.3243306. Epub 2023 Jun 30.
Scene graph generation aims to interpret an input image by explicitly modelling the objects contained therein and their relationships. In existing methods the problem is predominantly solved by message passing neural network models. Unfortunately, in such models, the variational distributions generally ignore the structural dependencies among the output variables, and most of the scoring functions only consider pairwise dependencies. This can lead to inconsistent interpretations. In this article, we propose a novel neural belief propagation method seeking to replace the traditional mean field approximation with a structural Bethe approximation. To find a better bias-variance trade-off, higher-order dependencies among three or more output variables are also incorporated into the relevant scoring function. The proposed method achieves the state-of-the-art performance on various popular scene graph generation benchmarks.
场景图生成旨在通过显式建模其中包含的对象及其关系来解释输入图像。在现有的方法中，这个问题主要通过消息传递神经网络模型来解决。不幸的是，在这样的模型中，变分分布通常忽略了输出变量之间的结构依赖关系，而大多数评分函数只考虑了两两依赖关系。这可能导致不一致的解释。在本文中，我们提出了一种新的神经信念传播方法，试图用结构贝叶斯逼近来替代传统的平均场逼近。为了找到更好的偏差-方差权衡，还将三个或更多输出变量之间的高阶依赖关系纳入到相关的评分函数中。该方法在各种流行的场景图生成基准上实现了最先进的性能。
