  One of the major challenges of style transfer is the appropriate image
features supervision between the output image and the input (style and content)
images. An efficient strategy would be to define an object map between the
objects of the style and the content images. However, such a mapping is not
well established when there are semantic objects of different types and numbers
in the style and the content images. It also leads to content mismatch in the
style transfer output, which could reduce the visual quality of the results. We
propose an object-based style transfer approach, called DeepObjStyle, for the
style supervision in the training data-independent framework. DeepObjStyle
preserves the semantics of the objects and achieves better style transfer in
the challenging scenario when the style and the content images have a mismatch
of image features. We also perform style transfer of images containing a word
cloud to demonstrate that DeepObjStyle enables an appropriate image features
supervision. We validate the results using quantitative comparisons and user
studies.
