sections/conclusion_future_work/social_impact.md

Our initial plan was to include 4 high-resource and 4 low-resource languages (Marathi, Bengali, Urdu, Telegu) in our training data. However, the existing translations do not perform as well and we would have received poor labels, not to mention, with a longer training time.

Being able to automatically describe the content of an image using properly formed sentences in any language is a challenging task, but it could have great impact by helping visually impaired people better understand their surroundings.

A slightly (not-so) long term use case would definitely be, explaining what happens in a video, frame by frame. One more recent use-case for the same can be generating surgical instructions. Since our model is multi-lingual which means the instructions will not be just limited to regions where English is spoken but those instructions can be perused in regions where Spanish, French and German are spoken as well. Further if we extend this project to low-resource languages then its impact can be manifold.