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This article describes the model we built that achieved 1st place in theOpenImage Visual Relationship Detection Challenge on Kaggle. Three key factorscontribute the most to our success: 1) language bias is a powerful baseline forthis task. We build the empirical distribution $P(predicate|subject,object)$ inthe training set and directly use that in testing. This baseline achieved the2nd place when submitted; 2) spatial features are as important as visualfeatures, especially for spatial relationships such as "under" and "inside of";3) It is a very effective way to fuse different features by first buildingseparate modules for each of them, then adding their output logits before thefinal softmax layer. We show in ablation study that each factor can improve theperformance to a non-trivial extent, and the model reaches optimal when all ofthem are combined.

Introduction to the 1st Place Winning Model of OpenImages Relationship Detection Challenge