  The quality of scene graphs generated by the state-of-the-art (SOTA) models
is compromised due to the long-tail nature of the relationships and their
parent object pairs. Training of the scene graphs is dominated by the majority
relationships of the majority pairs and, therefore, the object-conditional
distributions of relationship in the minority pairs are not preserved after the
training is converged. Consequently, the biased model performs well on more
frequent relationships in the marginal distribution of relationships such as
`on' and `wearing', and performs poorly on the less frequent relationships such
as `eating' or `hanging from'. In this work, we propose virtual evidence
incorporated within-triplet Bayesian Network (BN) to preserve the
object-conditional distribution of the relationship label and to eradicate the
bias created by the marginal probability of the relationships. The insufficient
number of relationships in the minority classes poses a significant problem in
learning the within-triplet Bayesian network. We address this insufficiency by
embedding-based augmentation of triplets where we borrow samples of the
minority triplet classes from its neighborhood triplets in the semantic space.
We perform experiments on two different datasets and achieve a significant
improvement in the mean recall of the relationships. We also achieve better
balance between recall and mean recall performance compared to the SOTA
de-biasing techniques of scene graph models.
