simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any
explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between
verbs and image regions corresponding to their arguments.
Tips:
Most of the checkpoints provided work with the [VisualBertForPreTraining] configuration. Other
   checkpoints provided are the fine-tuned checkpoints for down-stream tasks - VQA ('visualbert-vqa'), VCR
   ('visualbert-vcr'), NLVR2 ('visualbert-nlvr2'). Hence, if you are not working on these downstream tasks, it is
   recommended that you use the pretrained checkpoints.
For the VCR task, the authors use a fine-tuned detector for generating visual embeddings, for all the checkpoints.
   We do not provide the detector and its weights as a part of the package, but it will be available in the research
