  Research in scene graph generation has quickly gained traction in the past
few years because of its potential to help in downstream tasks like visual
question answering, image captioning, etc. Many interesting approaches have
been proposed to tackle this problem. Most of these works have a pre-trained
object detection model as a preliminary feature extractor. Therefore, getting
object bounding box proposals from the object detection model is relatively
cheaper. We take advantage of this ready availability of bounding box
annotations produced by the pre-trained detector. We propose a set of three
novel yet simple self-supervision tasks and train them as auxiliary multi-tasks
to the main model. While comparing, we train the base-model from scratch with
these self-supervision tasks, we achieve state-of-the-art results in all the
metrics and recall settings. We also resolve some of the confusion between two
types of relationships: geometric and possessive, by training the model with
the proposed self-supervision losses. We use the benchmark dataset, Visual
Genome to conduct our experiments and show our results.
