  For a typical Scene Graph Generation (SGG) method, there is often a large gap
in the performance of the predicates' head classes and tail classes. This
phenomenon is mainly caused by the semantic overlap between different
predicates as well as the long-tailed data distribution. In this paper, a
Predicate Correlation Learning (PCL) method for SGG is proposed to address the
above two problems by taking the correlation between predicates into
consideration. To describe the semantic overlap between strong-correlated
predicate classes, a Predicate Correlation Matrix (PCM) is defined to quantify
the relationship between predicate pairs, which is dynamically updated to
remove the matrix's long-tailed bias. In addition, PCM is integrated into a
Predicate Correlation Loss function ($L_{PC}$) to reduce discouraging gradients
of unannotated classes. The proposed method is evaluated on Visual Genome
benchmark, where the performance of the tail classes is significantly improved
when built on the existing methods.
