  With great advances in vision and natural language processing, the generation
of image captions becomes a need. In a recent paper, Mathews, Xie and He [1],
extended a new model to generate styled captions by separating semantics and
style. In continuation of this work, here a new captioning model is developed
including an image encoder to extract the features, a mixture of recurrent
networks to embed the set of extracted features to a set of words, and a
sentence generator that combines the obtained words as a stylized sentence. The
resulted system that entitled as Mixture of Recurrent Experts (MoRE), uses a
new training algorithm that derives singular value decomposition (SVD) from
weighting matrices of Recurrent Neural Networks (RNNs) to increase the
diversity of captions. Each decomposition step depends on a distinctive factor
based on the number of RNNs in MoRE. Since the used sentence generator gives a
stylized language corpus without paired images, our captioning model can do the
same. Besides, the styled and diverse captions are extracted without training
on a densely labeled or styled dataset. To validate this captioning model, we
use Microsoft COCO which is a standard factual image caption dataset. We show
that the proposed captioning model can generate a diverse and stylized image
captions without the necessity of extra-labeling. The results also show better
descriptions in terms of content accuracy.
