  Image captioning is the process of automatically generating a description of
an image in natural language. Image captioning is one of the significant
challenges in image understanding since it requires not only recognizing
salient objects in the image but also their attributes and the way they
interact. The system must then generate a syntactically and semantically
correct caption that describes the image content in natural language. With the
significant progress in deep learning models and their ability to effectively
encode large sets of images and generate correct sentences, several
neural-based captioning approaches have been proposed recently, each trying to
achieve better accuracy and caption quality. This paper introduces an
encoder-decoder-based image captioning system in which the encoder extracts
spatial features from the image using ResNet-101. This stage is followed by a
refining model, which uses an attention-on-attention mechanism to extract the
visual features of the target image objects, then determine their interactions.
The decoder consists of an attention-based recurrent module and a reflective
attention module, which collaboratively apply attention to the visual and
textual features to enhance the decoder's ability to model long-term sequential
dependencies. Extensive experiments performed on Flickr30K, show the
effectiveness of the proposed approach and the high quality of the generated
captions.
