Add references and news

apps/article.py

@@ -3,295 +3,6 @@ from apps.utils import read_markdown
 from .streamlit_tensorboard import st_tensorboard, kill_tensorboard
 from .utils import Toc
 
-def bias_examples():
-    # Gender
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-
-    col1.write("")
-    col2.image("./sections/bias_examples/female_cricketer.jpeg", use_column_width='always', caption="https://www.crictracker.com/wp-content/uploads/2018/06/Sarah-Taylor-1.jpg")
-
-    col3.image("./sections/bias_examples/male_cricketer.jpeg", use_column_width='always', caption="https://www.cricket.com.au/~/-/media/News/2019/02/11pucovskiw.ashx?w=1600")
-    
-    col4.image("./sections/bias_examples/male_cricketer_indian.jpeg", use_column_width='always', caption="https://tse4.mm.bing.net/th?id=OIP.FOdOQvpiFA_HE32pA0zB-QHaEd&pid=Api")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-
-    col1.write("**What is the sex of the person?**")
-    col2.write("Female")
-    col3.write("Female")
-    col4.write("Male")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Cual es el sexo de la persona?")
-    col2.write("mujer")
-    col3.write("mujer")
-    col4.write("masculino")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Quel est le sexe de la personne ?")
-    col2.write("femelle")
-    col3.write("femelle")
-    col4.write("Masculin")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Welches Geschlecht hat die Person?")
-    col2.write("weiblich")
-    col3.write("mannlich")
-    col4.write("mannlich")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("**Is this person male?**")
-    col2.write("yes")
-    col3.write("yes")
-    col4.write("yes")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("¿Esta persona es hombre?")
-    col2.write("si")
-    col3.write("si")
-    col4.write("si")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Cette personne est-elle un homme ?")
-    col2.write("Oui")
-    col3.write("Oui")
-    col4.write("Oui")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Ist diese Person männlich?")
-    col2.write("Ja")
-    col3.write("Ja")
-    col4.write("Ja")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("**Is this person female?**")
-    col2.write("no")
-    col3.write("yes")
-    col4.write("yes")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("¿Esta persona es mujer?")
-    col2.write("si")
-    col3.write("si")
-    col4.write("si")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Cette personne est-elle un femme ?")
-    col2.write("Oui")
-    col3.write("Oui")
-    col4.write("Oui")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Ist diese Person weiblich?")
-    col2.write("Nein")
-    col3.write("Ja")
-    col4.write("Ja")
-
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("**Do you think this person is male or female?**")
-    col2.write("female")
-    col3.write("female")
-    col4.write("male")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("¿Crees que esta persona es hombre o mujer?")
-    col2.write("mujer")
-    col3.write("mujer")
-    col4.write("masculino")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Pensez-vous que cette personne est un homme ou une femme ?")
-    col2.write("femelle")
-    col3.write("Masculin")
-    col4.write("femelle")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Glaubst du, diese Person ist männlich oder weiblich?")
-    col2.write("weiblich")
-    col3.write("weiblich")
-    col4.write("mannlich")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("**Is this cricketer male or female?**")
-    col2.write("female")
-    col3.write("female")
-    col4.write("male")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("¿Este jugador de críquet es hombre o mujer?")
-    col2.write("mujer")
-    col3.write("mujer")
-    col4.write("masculino")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Ce joueur de cricket est-il un homme ou une femme ?")
-    col2.write("femelle")
-    col3.write("femelle")
-    col4.write("femelle")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Ist dieser Cricketspieler männlich oder weiblich?")
-    col2.write("weiblich")
-    col3.write("mannlich")
-    col4.write("mannlich")
-
-    # Programmmer
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-
-    col1.write("")
-    col2.image("./sections/bias_examples/female_programmer.jpeg", use_column_width='always', caption="https://tse4.mm.bing.net/th?id=OIP.GZ3Ol84W4UcOpVR9oawWygHaE7&pid=Api")
-
-    col3.image("./sections/bias_examples/male_programmer.jpeg", use_column_width='always', caption="https://thumbs.dreamstime.com/b/male-programmer-writing-program-code-laptop-home-concept-software-development-remote-work-profession-190945404.jpg")
-    
-    col4.image("./sections/bias_examples/female_programmer_short_haired.jpeg", use_column_width='always', caption="https://media.istockphoto.com/photos/profile-view-of-young-female-programmer-working-on-computer-software-picture-id1125595211")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-
-    col1.write("**What is the sex of the person?**")
-    col2.write("Female")
-    col3.write("Male")
-    col4.write("female")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Cual es el sexo de la persona?")
-    col2.write("mujer")
-    col3.write("masculino")
-    col4.write("mujer")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Quel est le sexe de la personne ?")
-    col2.write("femelle")
-    col3.write("Masculin")
-    col4.write("femelle")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Welches Geschlecht hat die Person?")
-    col2.write("weiblich")
-    col3.write("mannlich")
-    col4.write("weiblich")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("**Is this person male?**")
-    col2.write("no")
-    col3.write("yes")
-    col4.write("no")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("¿Esta persona es hombre?")
-    col2.write("no")
-    col3.write("si")
-    col4.write("no")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Cette personne est-elle un homme ?")
-    col2.write("non")
-    col3.write("Oui")
-    col4.write("non")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Ist diese Person männlich?")
-    col2.write("Nein")
-    col3.write("Ja")
-    col4.write("Nein")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("**Is this person female?**")
-    col2.write("yes")
-    col3.write("no")
-    col4.write("yes")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("¿Esta persona es mujer?")
-    col2.write("si")
-    col3.write("no")
-    col4.write("si")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Cette personne est-elle un femme ?")
-    col2.write("Oui")
-    col3.write("non")
-    col4.write("Oui")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Ist diese Person weiblich?")
-    col2.write("Nein")
-    col3.write("Nein")
-    col4.write("Nein")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("**Do you think this person is male or female?**")
-    col2.write("female")
-    col3.write("male")
-    col4.write("female")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("¿Crees que esta persona es hombre o mujer?")
-    col2.write("mujer")
-    col3.write("masculino")
-    col4.write("mujer")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Pensez-vous que cette personne est un homme ou une femme ?")
-    col2.write("femelle")
-    col3.write("masculin")
-    col4.write("femelle")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Glaubst du, diese Person ist männlich oder weiblich?")
-    col2.write("weiblich")
-    col3.write("mannlich")
-    col4.write("weiblich")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("**Is this programmer male or female?**")
-    col2.write("female")
-    col3.write("male")
-    col4.write("female")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("¿Este programador es hombre o mujer?")
-    col2.write("mujer")
-    col3.write("masculino")
-    col4.write("mujer")
-
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Ce programmeur est-il un homme ou une femme ?")
-    col2.write("femme")
-    col3.write("homme")
-    col4.write("femme")
-
-    col1, col2, col3, col4 = st.beta_columns([1,1,1,1])
-    col1.write("Ist dieser Programmierer männlich oder weiblich?")
-    col2.write("weiblich")
-    col3.write("mannlich")
-    col4.write("weiblich")
 
     
 def app(state=None):
@@ -301,8 +12,9 @@ def app(state=None):
     
     st.header("Table of Contents")
     toc.placeholder()
-    
+
     toc.header("Introduction and Motivation")
+    st.info("**News**: Two days back, a paper using CLIP-Vision and BERT has been posted on arXiv! The paper uses LXMERT objects and achieves 80% on the English VQAv2 dataset. It would be interesting to see how it performs on our multilingual dataset. Check it out here: https://arxiv.org/pdf/2107.06383.pdf")
     st.write(read_markdown("intro/intro_part_1.md"))
     with st.beta_expander("FasterRCNN Approach"):
         st.write(read_markdown("intro/faster_rcnn_approach.md"))
@@ -345,10 +57,8 @@ def app(state=None):
     toc.header("Challenges and Technical Difficulties")
     st.write(read_markdown("challenges.md"))
     
-    toc.header("Limitations")
+    toc.header("Limitations and Bias")
     st.write(read_markdown("limitations.md"))
-
-    #bias_examples()
     
     toc.header("Conclusion, Future Work, and Social Impact")
     # toc.subheader("Conclusion")
@@ -359,7 +69,11 @@ def app(state=None):
     st.write(read_markdown("conclusion_future_work/social_impact.md"))
     
     toc.header("References")
-    st.write(read_markdown("references.md"))
+    toc.subheader("Papers")
+    st.write(read_markdown("references/papers.md"))
+    toc.subheader("Useful Links")
+    st.write(read_markdown("references/useful_links.md"))
+
 
     toc.header("Checkpoints")
     st.write(read_markdown("checkpoints/checkpoints.md"))

sections/references/papers.md

@@ -0,0 +1,78 @@
+```
+@inproceedings{wolf-etal-2020-transformers,
+    title = "Transformers: State-of-the-Art Natural Language Processing",
+    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+    month = oct,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+    pages = "38--45"
+}
+```
+```
+@inproceedings{changpinyo2021cc12m,
+  title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
+  author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
+  booktitle = {CVPR},
+  year = {2021},
+}
+```
+```
+@InProceedings{mariannmt,
+  title     = {Marian: Fast Neural Machine Translation in {C++}},
+  author    = {Junczys-Dowmunt, Marcin and Grundkiewicz, Roman and
+               Dwojak, Tomasz and Hoang, Hieu and Heafield, Kenneth and
+               Neckermann, Tom and Seide, Frank and Germann, Ulrich and
+               Fikri Aji, Alham and Bogoychev, Nikolay and
+               Martins, Andr\'{e} F. T. and Birch, Alexandra},
+  booktitle = {Proceedings of ACL 2018, System Demonstrations},
+  pages     = {116--121},
+  publisher = {Association for Computational Linguistics},
+  year      = {2018},
+  month     = {July},
+  address   = {Melbourne, Australia},
+  url       = {http://www.aclweb.org/anthology/P18-4020}
+}
+```
+```
+@misc{agrawal2016vqa,
+      title={VQA: Visual Question Answering}, 
+      author={Aishwarya Agrawal and Jiasen Lu and Stanislaw Antol and Margaret Mitchell and C. Lawrence Zitnick and Dhruv Batra and Devi Parikh},
+      year={2016},
+      eprint={1505.00468},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+```
+@misc{li2019visualbert,
+      title={VisualBERT: A Simple and Performant Baseline for Vision and Language}, 
+      author={Liunian Harold Li and Mark Yatskar and Da Yin and Cho-Jui Hsieh and Kai-Wei Chang},
+      year={2019},
+      eprint={1908.03557},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+```
+@misc{vaswani2017attention,
+      title={Attention Is All You Need}, 
+      author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
+      year={2017},
+      eprint={1706.03762},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+```
+@misc{radford2021learning,
+      title={Learning Transferable Visual Models From Natural Language Supervision},
+      author={Alec Radford and Jong Wook Kim and Chris Hallacy and Aditya Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
+      year={2021},
+      eprint={2103.00020},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```