  Scene graphs are nodes and edges consisting of objects and object-object
relationships, respectively. Scene graph generation (SGG) aims to identify the
objects and their relationships. We propose a bidirectional GRU (BiGRU)
transformer network (BGT-Net) for the scene graph generation for images. This
model implements novel object-object communication to enhance the object
information using a BiGRU layer. Thus, the information of all objects in the
image is available for the other objects, which can be leveraged later in the
object prediction step. This object information is used in a transformer
encoder to predict the object class as well as to create object-specific edge
information via the use of another transformer encoder. To handle the dataset
bias induced by the long-tailed relationship distribution, softening with a
log-softmax function and adding a bias adaptation term to regulate the bias for
every relation prediction individually showed to be an effective approach. We
conducted an elaborate study on experiments and ablations using open-source
datasets, i.e., Visual Genome, Open-Images, and Visual Relationship Detection
datasets, demonstrating the effectiveness of the proposed model over state of
the art.
