119 lines
5.0 KiB
Markdown
119 lines
5.0 KiB
Markdown
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# LXMERT
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## Overview
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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
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(one for the vision modality, one for the language modality, and then one to fuse both modalities) pretrained using a
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combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked
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visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. The pretraining
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consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA.
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The abstract from the paper is the following:
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*Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly,
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the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality
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Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we
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build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language
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encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language
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semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative
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pretraining tasks: masked language modeling, masked object prediction (feature regression and label classification),
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cross-modality matching, and image question answering. These tasks help in learning both intra-modality and
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cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art
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results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our
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pretrained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR, and improve the previous
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best result by 22% absolute (54% to 76%). Lastly, we demonstrate detailed ablation studies to prove that both our novel
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model components and pretraining strategies significantly contribute to our strong results; and also present several
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attention visualizations for the different encoders*
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This model was contributed by [eltoto1219](https://huggingface.co/eltoto1219). The original code can be found [here](https://github.com/airsplay/lxmert).
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## Usage tips
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- Bounding boxes are not necessary to be used in the visual feature embeddings, any kind of visual-spacial features
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will work.
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- Both the language hidden states and the visual hidden states that LXMERT outputs are passed through the
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cross-modality layer, so they contain information from both modalities. To access a modality that only attends to
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itself, select the vision/language hidden states from the first input in the tuple.
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- The bidirectional cross-modality encoder attention only returns attention values when the language modality is used
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as the input and the vision modality is used as the context vector. Further, while the cross-modality encoder
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contains self-attention for each respective modality and cross-attention, only the cross attention is returned and
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both self attention outputs are disregarded.
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## Resources
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- [Question answering task guide](../tasks/question_answering)
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## LxmertConfig
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[[autodoc]] LxmertConfig
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## LxmertTokenizer
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[[autodoc]] LxmertTokenizer
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## LxmertTokenizerFast
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[[autodoc]] LxmertTokenizerFast
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## Lxmert specific outputs
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[[autodoc]] models.lxmert.modeling_lxmert.LxmertModelOutput
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[[autodoc]] models.lxmert.modeling_lxmert.LxmertForPreTrainingOutput
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[[autodoc]] models.lxmert.modeling_lxmert.LxmertForQuestionAnsweringOutput
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[[autodoc]] models.lxmert.modeling_tf_lxmert.TFLxmertModelOutput
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[[autodoc]] models.lxmert.modeling_tf_lxmert.TFLxmertForPreTrainingOutput
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<frameworkcontent>
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<pt>
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## LxmertModel
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[[autodoc]] LxmertModel
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- forward
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## LxmertForPreTraining
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[[autodoc]] LxmertForPreTraining
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- forward
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## LxmertForQuestionAnswering
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[[autodoc]] LxmertForQuestionAnswering
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- forward
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</pt>
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<tf>
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## TFLxmertModel
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[[autodoc]] TFLxmertModel
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- call
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## TFLxmertForPreTraining
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[[autodoc]] TFLxmertForPreTraining
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- call
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</tf>
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</frameworkcontent>
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