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2024年4月12日发(作者：安装mysqlclient报错)

a survey of visionlanguage pre-trained models

Sure, here is a survey of some popular vision-language pre-trained

models:

1. ViT (Vision Transformer): ViT is a widely used vision-language

pre-trained model that applies the transformer architecture to both

images and texts. It has achieved state-of-the-art results on several

vision-language tasks, such as image-text retrieval and visual

question answering.

2. DeVLBERT: DeVLBERT is a dual-stream transformer-based

model that fuses visual and textual features. It utilizes separate

transformer encoders for images and texts and incorporates cross-

modal attention mechanisms to enable effective integration of the

two modalities.

3. UNITER: UNITER is a unified transformer model that performs

joint reasoning over images and texts. It promotes cross-modal

understanding through cross-modal attention and context fusion

mechanisms. UNITER has shown strong performance on various

vision-language benchmarks.

4. LXMERT: LXMERT is a vision-language pre-trained model

that combines a large-scale transformer with a multimodal encoder.

It incorporates both image and text encoders and utilizes cross-

modal matching mechanisms to capture fine-grained correlations

between vision and language.

5. VisualBERT: VisualBERT is a transformer-based model that

extends the BERT architecture to perform vision-language tasks. It

utilizes a combination of image and textual embeddings and

introduces bi-directional cross-modal attention modules to align

visual and textual information.

6. OSCAR: OSCAR is a large-scale pre-trained model that

combines image and text inputs for vision-language modeling. It

employs a masked language modeling objective to pre-train on

large-scale paired image-text data and has achieved competitive

performance on various vision-language tasks.

These are just a few examples of vision-language pre-trained

models. Each model has its unique characteristics and architecture

to handle the fusion of vision and language modalities. Researchers

and practitioners can choose from these options based on their

specific needs and requirements.


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