  'Actions' play a vital role in how humans interact with the world. Thus,
autonomous agents that would assist us in everyday tasks also require the
capability to perform 'Reasoning about Actions & Change' (RAC). Recently, there
has been growing interest in the study of RAC with visual and linguistic
inputs. Graphs are often used to represent semantic structure of the visual
content (i.e. objects, their attributes and relationships among objects),
commonly referred to as scene-graphs. In this work, we propose a novel method
that leverages scene-graph representation of images to reason about the effects
of actions described in natural language. We experiment with existing CLEVR_HYP
(Sampat et. al, 2021) dataset and show that our proposed approach is effective
in terms of performance, data efficiency, and generalization capability
compared to existing models.
