  Scene graphs -- objects as nodes and visual relationships as edges --
describe the whereabouts and interactions of the things and stuff in an image
for comprehensive scene understanding. To generate coherent scene graphs,
almost all existing methods exploit the fruitful visual context by modeling
message passing among objects, fitting the dynamic nature of reasoning with
visual context, eg, "person" on "bike" can help to determine the relationship
"ride", which in turn contributes to the category confidence of the two
objects. However, we argue that the scene dynamics is not properly learned by
using the prevailing cross-entropy based supervised learning paradigm, which is
not sensitive to graph inconsistency: errors at the hub or non-hub nodes are
unfortunately penalized equally. To this end, we propose a Counterfactual
critic Multi-Agent Training (CMAT) approach to resolve the mismatch. CMAT is a
multi-agent policy gradient method that frames objects as cooperative agents,
and then directly maximizes a graph-level metric as the reward. In particular,
to assign the reward properly to each agent, CMAT uses a counterfactual
baseline that disentangles the agent-specific reward by fixing the dynamics of
other agents. Extensive validations on the challenging Visual Genome benchmark
show that CMAT achieves a state-of-the-art by significant performance gains
under various settings and metrics.
