  Scene graph generation (SGG) aims to detect objects in an image along with
their pairwise relationships. There are three key properties of scene graph
that have been underexplored in recent works: namely, the edge direction
information, the difference in priority between nodes, and the long-tailed
distribution of relationships. Accordingly, in this paper, we propose a Graph
Property Sensing Network (GPS-Net) that fully explores these three properties
for SGG. First, we propose a novel message passing module that augments the
node feature with node-specific contextual information and encodes the edge
direction information via a tri-linear model. Second, we introduce a node
priority sensitive loss to reflect the difference in priority between nodes
during training. This is achieved by designing a mapping function that adjusts
the focusing parameter in the focal loss. Third, since the frequency of
relationships is affected by the long-tailed distribution problem, we mitigate
this issue by first softening the distribution and then enabling it to be
adjusted for each subject-object pair according to their visual appearance.
Systematic experiments demonstrate the effectiveness of the proposed
techniques. Moreover, GPS-Net achieves state-of-the-art performance on three
popular databases: VG, OI, and VRD by significant gains under various settings
and metrics. The code and models are available at
\url{https://github.com/taksau/GPS-Net}.
