  Scene graph generation (SGG) is designed to extract (subject, predicate,
object) triplets in images. Recent works have made a steady progress on SGG,
and provide useful tools for high-level vision and language understanding.
However, due to the data distribution problems including long-tail distribution
and semantic ambiguity, the predictions of current SGG models tend to collapse
to several frequent but uninformative predicates (e.g., on, at), which limits
practical application of these models in downstream tasks. To deal with the
problems above, we propose a novel Internal and External Data Transfer
(IETrans) method, which can be applied in a plug-and-play fashion and expanded
to large SGG with 1,807 predicate classes. Our IETrans tries to relieve the
data distribution problem by automatically creating an enhanced dataset that
provides more sufficient and coherent annotations for all predicates. By
training on the enhanced dataset, a Neural Motif model doubles the macro
performance while maintaining competitive micro performance. The code and data
are publicly available at https://github.com/waxnkw/IETrans-SGG.pytorch.
