future scope

app.py

@@ -87,12 +87,13 @@ with st.beta_expander("Article"):
     st.write(read_markdown("abstract.md"))
     st.write(read_markdown("caveats.md"))
     # st.write("# Methodology")
-    # st.image(
-    #     "./misc/Multilingual-IC.png", caption="Seq2Seq model for Image-text Captioning."
-    # )
+    st.image(
+        "./misc/Multilingual-IC.png", caption="Seq2Seq model for Image-text Captioning."
+    )
     st.markdown(read_markdown("pretraining.md"))
     st.write(read_markdown("challenges.md"))
     st.write(read_markdown("social_impact.md"))
+    st.write(read_markdown("future_scope.md"))
     st.write(read_markdown("references.md"))
     # st.write(read_markdown("checkpoints.md"))
     st.write(read_markdown("acknowledgements.md"))

sections/future_scope.md

@@ -1,4 +1,5 @@
 # Future scope of work
-We hope to improve this in the future by using:
-- Better translating options. Better translators (for e.g. Google Translate API, Large pre-trained seq2seq models for translation) to get more multilingual data, especially in low-resource languages.
-- More training time: We found that training image captioning model for a single model takes a lot of compute time and if we want
+We hope to improve this project in the future by using:
+- Better translating options: Translation has a very huge impact on how the end model would perform. Better translators (for e.g. Google Translate API) and language specific seq2seq models for translation are able to generate better data, especially in low-resource languages.
+- More training time: We found that training image captioning model for a single model takes a lot of compute time and if we want to replicate the same then the training time goes up manifold for the same number of samples.
+- Accessibility: Make model deployable on hand-held devices to make it more accessible. Currently, our model is too large because of which not many will be able to access it. However, our final goal is ensure everyone can access it without any computation barriers. We got to know that JAX has an experimental converter `jax2tf`to convert JAX functions to TF. I hope we'll be able to support TFLite support for our model as well in future.