results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our
pretrained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR, and improve the previous
best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel
model components and pretraining strategies significantly contribute to our strong results; and also present several
attention visualizations for the different encoders
Tips:
Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features
  will work.
Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the
  cross-modality layer, so they contain information from both modalities. To access a modality that only attends to
