Doubly Reparameterized Importance Weighted Structure Learning for Scene Graph Generation. (arXiv:2206.11352v1 [cs.CV])

As a structured prediction task, scene graph generation, given an input
image, aims to explicitly model objects and their relationships by constructing
a visually-grounded scene graph. In the current literature, such task is
universally solved via a message passing neural network based mean field
variational Bayesian methodology. The classical loose evidence lower bound is
generally chosen as the variational inference objective, which could induce
oversimplified variational approximation and thus underestimate the underlying
complex posterior. In this paper, we propose a …

arxiv cv generation graph importance learning