  Today, scene graph generation(SGG) task is largely limited in realistic
scenarios, mainly due to the extremely long-tailed bias of predicate annotation
distribution. Thus, tackling the class imbalance trouble of SGG is critical and
challenging. In this paper, we first discover that when predicate labels have
strong correlation with each other, prevalent re-balancing strategies(e.g.,
re-sampling and re-weighting) will give rise to either over-fitting the tail
data(e.g., bench sitting on sidewalk rather than on), or still suffering the
adverse effect from the original uneven distribution(e.g., aggregating varied
parked on/standing on/sitting on into on). We argue the principal reason is
that re-balancing strategies are sensitive to the frequencies of predicates yet
blind to their relatedness, which may play a more important role to promote the
learning of predicate features. Therefore, we propose a novel
Predicate-Correlation Perception Learning(PCPL for short) scheme to adaptively
seek out appropriate loss weights by directly perceiving and utilizing the
correlation among predicate classes. Moreover, our PCPL framework is further
equipped with a graph encoder module to better extract context features.
Extensive experiments on the benchmark VG150 dataset show that the proposed
PCPL performs markedly better on tail classes while well-preserving the
performance on head ones, which significantly outperforms previous
state-of-the-art methods.
