  An unbiased scene graph generation (SGG) algorithm referred to as Skew
Class-balanced Re-weighting (SCR) is proposed for considering the unbiased
predicate prediction caused by the long-tailed distribution. The prior works
focus mainly on alleviating the deteriorating performances of the minority
predicate predictions, showing drastic dropping recall scores, i.e., losing the
majority predicate performances. It has not yet correctly analyzed the
trade-off between majority and minority predicate performances in the limited
SGG datasets. In this paper, to alleviate the issue, the Skew Class-balanced
Re-weighting (SCR) loss function is considered for the unbiased SGG models.
Leveraged by the skewness of biased predicate predictions, the SCR estimates
the target predicate weight coefficient and then re-weights more to the biased
predicates for better trading-off between the majority predicates and the
minority ones. Extensive experiments conducted on the standard Visual Genome
dataset and Open Image V4 \& V6 show the performances and generality of the SCR
with the traditional SGG models.
