  Automatic transcription of scene understanding in images and videos is a step
towards artificial general intelligence. Image captioning is a nomenclature for
describing meaningful information in an image using computer vision techniques.
Automated image captioning techniques utilize encoder and decoder architecture,
where the encoder extracts features from an image and the decoder generates a
transcript. In this work, we investigate two unexplored ideas for image
captioning using transformers: First, we demonstrate the enforcement of using
objects' relevance in the surrounding environment. Second, learning an explicit
association between labels and language constructs. We propose label-attention
Transformer with geometrically coherent objects (LATGeO). The proposed
technique acquires a proposal of geometrically coherent objects using a deep
neural network (DNN) and generates captions by investigating their
relationships using a label-attention module. Object coherence is defined using
the localized ratio of the geometrical properties of the proposals. The
label-attention module associates the extracted objects classes to the
available dictionary using self-attention layers. The experimentation results
show that objects' relevance in surroundings and binding of their visual
feature with their geometrically localized ratios combined with its associated
labels help in defining meaningful captions. The proposed framework is tested
on the MSCOCO dataset, and a thorough evaluation resulting in overall better
quantitative scores pronounces its superiority.
