  Image captioning is the task of automatically generating sentences that
describe an input image in the best way possible. The most successful
techniques for automatically generating image captions have recently used
attentive deep learning models. There are variations in the way deep learning
models with attention are designed. In this survey, we provide a review of
literature related to attentive deep learning models for image captioning.
Instead of offering a comprehensive review of all prior work on deep image
captioning models, we explain various types of attention mechanisms used for
the task of image captioning in deep learning models. The most successful deep
learning models used for image captioning follow the encoder-decoder
architecture, although there are differences in the way these models employ
attention mechanisms. Via analysis on performance results from different
attentive deep models for image captioning, we aim at finding the most
successful types of attention mechanisms in deep models for image captioning.
Soft attention, bottom-up attention, and multi-head attention are the types of
attention mechanism widely used in state-of-the-art attentive deep learning
models for image captioning. At the current time, the best results are achieved
from variants of multi-head attention with bottom-up attention.
