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+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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+
+# VisualBERT
+
+## Overview
+
+The VisualBERT model was proposed in [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
+VisualBERT is a neural network trained on a variety of (image, text) pairs.
+
+The abstract from the paper is the following:
+
+*We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks.
+VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an
+associated input image with self-attention. We further propose two visually-grounded language model objectives for
+pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2,
+and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly
+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:
+
+1. 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.
+
+2. 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
+   projects, and the states can be loaded directly into the detector provided.
+
+## Usage
+
+VisualBERT is a multi-modal vision and language model. It can be used for visual question answering, multiple choice,
+visual reasoning and region-to-phrase correspondence tasks. VisualBERT uses a BERT-like transformer to prepare
+embeddings for image-text pairs. Both the text and visual features are then projected to a latent space with identical
+dimension.
+
+To feed images to the model, each image is passed through a pre-trained object detector and the regions and the
+bounding boxes are extracted. The authors use the features generated after passing these regions through a pre-trained
+CNN like ResNet as visual embeddings. They also add absolute position embeddings, and feed the resulting sequence of
+vectors to a standard BERT model. The text input is concatenated in the front of the visual embeddings in the embedding
+layer, and is expected to be bound by [CLS] and a [SEP] tokens, as in BERT. The segment IDs must also be set
+appropriately for the textual and visual parts.
+
+The [`BertTokenizer`] is used to encode the text. A custom detector/feature extractor must be used
+to get the visual embeddings. The following example notebooks show how to use VisualBERT with Detectron-like models:
+
+- [VisualBERT VQA demo notebook](https://github.com/huggingface/transformers/tree/master/examples/research_projects/visual_bert) : This notebook
+  contains an example on VisualBERT VQA.
+
+- [Generate Embeddings for VisualBERT (Colab Notebook)](https://colab.research.google.com/drive/1bLGxKdldwqnMVA5x4neY7-l_8fKGWQYI?usp=sharing) : This notebook contains
+  an example on how to generate visual embeddings.
+
+The following example shows how to get the last hidden state using [`VisualBertModel`]:
+
+```python
+>>> import torch
+>>> from transformers import BertTokenizer, VisualBertModel
+
+>>> model = VisualBertModel.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
+>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+>>> inputs = tokenizer("What is the man eating?", return_tensors="pt")
+>>> # this is a custom function that returns the visual embeddings given the image path
+>>> visual_embeds = get_visual_embeddings(image_path)
+
+>>> visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
+>>> visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
+>>> inputs.update({
+...     "visual_embeds": visual_embeds,
+...     "visual_token_type_ids": visual_token_type_ids,
+...     "visual_attention_mask": visual_attention_mask
+... })
+>>> outputs = model(**inputs)
+>>> last_hidden_state = outputs.last_hidden_state
+```
+
+This model was contributed by [gchhablani](https://huggingface.co/gchhablani). The original code can be found [here](https://github.com/uclanlp/visualbert).
+
+## VisualBertConfig
+
+[[autodoc]] VisualBertConfig
+
+## VisualBertModel
+
+[[autodoc]] VisualBertModel
+    - forward
+
+## VisualBertForPreTraining
+
+[[autodoc]] VisualBertForPreTraining
+    - forward
+
+## VisualBertForQuestionAnswering
+
+[[autodoc]] VisualBertForQuestionAnswering
+    - forward
+
+## VisualBertForMultipleChoice
+
+[[autodoc]] VisualBertForMultipleChoice
+    - forward
+
+## VisualBertForVisualReasoning
+
+[[autodoc]] VisualBertForVisualReasoning
+    - forward
+
+## VisualBertForRegionToPhraseAlignment
+
+[[autodoc]] VisualBertForRegionToPhraseAlignment
+    - forward