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+*This model was published in HF papers on 2019-08-20 and contributed to Hugging Face Transformers on 2020-11-16.*
+
+# LXMERT
+
+
+## Overview
+
+The LXMERT model was proposed in [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://huggingface.co/papers/1908.07490) by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders
+(one for the vision modality, one for the language modality, and then one to fuse both modalities) pretrained using a
+combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked
+visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. The pretraining
+consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.
+
+The abstract from the paper is the following:
+
+*Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly,
+the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality
+Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we
+build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language
+encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language
+semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative
+pretraining tasks: masked language modeling, masked object prediction (feature regression and label classification),
+cross-modality matching, and image question answering. These tasks help in learning both intra-modality and
+cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art
+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*
+
+This model was contributed by [eltoto1219](https://huggingface.co/eltoto1219). The original code can be found [here](https://github.com/airsplay/lxmert).
+
+## Usage 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
+  itself, select the vision/language hidden states from the first input in the tuple.
+- The bidirectional cross-modality encoder attention only returns attention values when the language modality is used
+  as the input and the vision modality is used as the context vector. Further, while the cross-modality encoder
+  contains self-attention for each respective modality and cross-attention, only the cross attention is returned and
+  both self attention outputs are disregarded.
+
+## Resources
+
+- [Question answering task guide](../tasks/question_answering)
+
+## LxmertConfig
+
+[[autodoc]] LxmertConfig
+
+## LxmertTokenizer
+
+[[autodoc]] LxmertTokenizer
+
+## LxmertTokenizerFast
+
+[[autodoc]] LxmertTokenizerFast
+
+## Lxmert specific outputs
+
+[[autodoc]] models.lxmert.modeling_lxmert.LxmertModelOutput
+
+[[autodoc]] models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput
+
+[[autodoc]] models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput
+
+## LxmertModel
+
+[[autodoc]] LxmertModel
+    - forward
+
+## LxmertForPreTraining
+
+[[autodoc]] LxmertForPreTraining
+    - forward
+
+## LxmertForQuestionAnswering
+
+[[autodoc]] LxmertForQuestionAnswering
+    - forward