  Identifying objects in an image and their mutual relationships as a scene
graph leads to a deep understanding of image content. Despite the recent
advancement in deep learning, the detection and labeling of visual object
relationships remain a challenging task. This work proposes a novel
local-context aware architecture named relation transformer, which exploits
complex global objects to object and object to edge (relation) interactions.
Our hierarchical multi-head attention-based approach efficiently captures
contextual dependencies between objects and predicts their relationships. In
comparison to state-of-the-art approaches, we have achieved an overall mean
\textbf{4.85\%} improvement and a new benchmark across all the scene graph
generation tasks on the Visual Genome dataset.
