  Visual Relationship Detection is defined as, given an image composed of a
subject and an object, the correct relation is predicted. To improve the visual
part of this difficult problem, ten preprocessing methods were tested to
determine whether the widely used Union method yields the optimal results.
Therefore, focusing solely on predicate prediction, no object detection and
linguistic knowledge were used to prevent them from affecting the comparison
results. Once fine-tuned, the Visual Geometry Group models were evaluated using
Recall@1, per-predicate recall, activation maximisations, class activation
maps, and error analysis. From this research it was found that using
preprocessing methods such as the Union-Without-Background-and-with-Binary-mask
(Union-WB-and-B) method yields significantly better results than the widely
used Union method since, as designed, it enables the Convolutional Neural
Network to also identify the subject and object in the convolutional layers
instead of solely in the fully-connected layers.
