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

BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation