  The continuous increase in the use of social media and the visual content on
the internet have accelerated the research in computer vision field in general
and the image captioning task in specific. The process of generating a caption
that best describes an image is a useful task for various applications such as
it can be used in image indexing and as a hearing aid for the visually
impaired. In recent years, the image captioning task has witnessed remarkable
advances regarding both datasets and architectures, and as a result, the
captioning quality has reached an astounding performance. However, the majority
of these advances especially in datasets are targeted for English, which left
other languages such as Arabic lagging behind. Although Arabic language, being
spoken by more than 450 million people and being the most growing language on
the internet, lacks the fundamental pillars it needs to advance its image
captioning research, such as benchmarks or unified datasets. This works is an
attempt to expedite the synergy in this task by providing unified datasets and
benchmarks, while also exploring methods and techniques that could enhance the
performance of Arabic image captioning. The use of multi-task learning is
explored, alongside exploring various word representations and different
features. The results showed that the use of multi-task learning and
pre-trained word embeddings noticeably enhanced the quality of image
captioning, however the presented results shows that Arabic captioning still
lags behind when compared to the English language. The used dataset and code
are available at this link.
