  Inspired by how the human brain employs more neural pathways when increasing
the focus on a subject, we introduce a novel twin cascaded attention model that
outperforms a state-of-the-art image captioning model that was originally
implemented using one channel of attention for the visual grounding task.
Visual grounding ensures the existence of words in the caption sentence that
are grounded into a particular region in the input image. After a deep learning
model is trained on visual grounding task, the model employs the learned
patterns regarding the visual grounding and the order of objects in the caption
sentences, when generating captions. We report the results of our experiments
in three image captioning tasks on the COCO dataset. The results are reported
using standard image captioning metrics to show the improvements achieved by
our model over the previous image captioning model. The results gathered from
our experiments suggest that employing more parallel attention pathways in a
deep neural network leads to higher performance. Our implementation of NTT is
publicly available at: https://github.com/zanyarz/NeuralTwinsTalk.
