  This article describes the model we built that achieved 1st place in the
OpenImage Visual Relationship Detection Challenge on Kaggle. Three key factors
contribute the most to our success: 1) language bias is a powerful baseline for
this task. We build the empirical distribution $P(predicate|subject,object)$ in
the training set and directly use that in testing. This baseline achieved the
2nd place when submitted; 2) spatial features are as important as visual
features, especially for spatial relationships such as "under" and "inside of";
3) It is a very effective way to fuse different features by first building
separate modules for each of them, then adding their output logits before the
final softmax layer. We show in ablation study that each factor can improve the
performance to a non-trivial extent, and the model reaches optimal when all of
them are combined.
