  Region visual features enhance the generative capability of the machines
based on features, however they lack proper interaction attentional perceptions
and thus ends up with biased or uncorrelated sentences or pieces of
misinformation. In this work, we propose Attribute Interaction-Tensor Product
Representation (aiTPR) which is a convenient way of gathering more information
through orthogonal combination and learning the interactions as physical
entities (tensors) and improving the captions. Compared to previous works,
where features are added up to undefined feature spaces, TPR helps in
maintaining sanity in combinations and orthogonality helps in defining familiar
spaces. We have introduced a new concept layer that defines the objects and
also their interactions that can play a crucial role in determination of
different descriptions. The interaction portions have contributed heavily for
better caption quality and has out-performed different previous works on this
domain and MSCOCO dataset. We introduced, for the first time, the notion of
combining regional image features and abstracted interaction likelihood
embedding for image captioning.
