  Image captioning is a computer vision task that involves generating natural
language descriptions for images. This method has numerous applications in
various domains, including image retrieval systems, medicine, and various
industries. However, while there has been significant research in image
captioning, most studies have focused on high quality images or controlled
environments, without exploring the challenges of real-world image captioning.
Real-world image captioning involves complex and dynamic environments with
numerous points of attention, with images which are often very poor in quality,
making it a challenging task, even for humans. This paper evaluates the
performance of various models that are built on top of different encoding
mechanisms, language decoders and training procedures using a newly created
real-world dataset that consists of over 800+ images of over 65 different scene
classes, built using MIT Indoor scenes dataset. This dataset is captioned using
the IC3 approach that generates more descriptive captions by summarizing the
details that are covered by standard image captioning models from unique
view-points of the image.
