  Scene graph generation aims to interpret an input image by explicitly
modelling the potential objects and their relationships, which is predominantly
solved by the message passing neural network models in previous methods.
Currently, such approximation models generally assume the output variables are
totally independent and thus ignore the informative structural higher-order
interactions. This could lead to the inconsistent interpretations for an input
image. In this paper, we propose a novel neural belief propagation method to
generate the resulting scene graph. It employs a structural Bethe approximation
rather than the mean field approximation to infer the associated marginals. To
find a better bias-variance trade-off, the proposed model not only incorporates
pairwise interactions but also higher order interactions into the associated
scoring function. It achieves the state-of-the-art performance on various
popular scene graph generation benchmarks.
