  We propose an efficient and interpretable scene graph generator. We consider
three types of features: visual, spatial and semantic, and we use a late fusion
strategy such that each feature's contribution can be explicitly investigated.
We study the key factors about these features that have the most impact on the
performance, and also visualize the learned visual features for relationships
and investigate the efficacy of our model. We won the champion of the
OpenImages Visual Relationship Detection Challenge on Kaggle, where we
outperform the 2nd place by 5\% (20\% relatively). We believe an accurate scene
graph generator is a fundamental stepping stone for higher-level
vision-language tasks such as image captioning and visual QA, since it provides
a semantic, structured comprehension of an image that is beyond pixels and
objects.
