lxmert.rst

LXMERT
----------------------------------------------------

Overview
~~~~~~~~~~~~~~~~~~~~~

The LXMERT model was proposed in `LXMERT: Learning Cross-Modality Encoder Representations from Transformers <https://arxiv.org/abs/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)
pre-trained 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 pre-training 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*

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 bi-directional 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.

The code can be found `here <https://github.com/airsplay/lxmert>`__


LxmertConfig
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.LxmertConfig
    :members:


LxmertTokenizer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.LxmertTokenizer
    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
        create_token_type_ids_from_sequences, save_vocabulary


Lxmert specific outputs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.modeling_lxmert.LxmertModelOutput
    :members:

.. autoclass:: transformers.modeling_lxmert.LxmertForPreTrainingOutput
    :members:

.. autoclass:: transformers.modeling_lxmert.LxmertForQuestionAnsweringOutput
    :members:

.. autoclass:: transformers.modeling_tf_lxmert.TFLxmertModelOutput
    :members:

.. autoclass:: transformers.modeling_tf_lxmert.TFLxmertForPreTrainingOutput
    :members:


LxmertModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.LxmertModel
    :members:

LxmertForPreTraining
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.LxmertForPreTraining
    :members:

LxmertForQuestionAnswering
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.LxmertForQuestionAnswering
    :members:


TFLxmertModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.TFLxmertModel
    :members:

TFLxmertForPreTraining
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. autoclass:: transformers.TFLxmertForPreTraining
    :members: