As a structured prediction task, scene graph generation aims to build a
visually-grounded scene graph to explicitly model objects and their
relationships in an input image. Currently, the mean field variational Bayesian
framework is the de facto methodology used by the existing methods, in which
the unconstrained inference step is often implemented by a message passing
neural network. However, such formulation fails to explore other inference
strategies, and largely ignores the more general constrained optimization
models. In this paper, we present a constrained structure learning method, for
which an explicit constrained variational inference objective is proposed.
Instead of applying the ubiquitous message-passing strategy, a generic
constrained optimization method - entropic mirror descent - is utilized to
solve the constrained variational inference step. We validate the proposed
generic model on various popular scene graph generation benchmarks and show
that it outperforms the state-of-the-art methods.

本文提出了一种使用约束结构学习和熵镜像下降方法的图像场景生成模型，相较于基于信息传递的方法，更能适应不同场景下的图像生成任务，并在各基准测试中取得了最优结果。