  Image captioning by the encoder-decoder framework has shown tremendous
advancement in the last decade where CNN is mainly used as encoder and LSTM is
used as a decoder. Despite such an impressive achievement in terms of accuracy
in simple images, it lacks in terms of time complexity and space complexity
efficiency. In addition to this, in case of complex images with a lot of
information and objects, the performance of this CNN-LSTM pair downgraded
exponentially due to the lack of semantic understanding of the scenes presented
in the images. Thus, to take these issues into consideration, we present
CNN-GRU encoder decode framework for caption-to-image reconstructor to handle
the semantic context into consideration as well as the time complexity. By
taking the hidden states of the decoder into consideration, the input image and
its similar semantic representations is reconstructed and reconstruction scores
from a semantic reconstructor are used in conjunction with likelihood during
model training to assess the quality of the generated caption. As a result, the
decoder receives improved semantic information, enhancing the caption
production process. During model testing, combining the reconstruction score
and the log-likelihood is also feasible to choose the most appropriate caption.
The suggested model outperforms the state-of-the-art LSTM-A5 model for picture
captioning in terms of time complexity and accuracy.
