65 lines
2.3 KiB
Markdown
65 lines
2.3 KiB
Markdown
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# VisionTextDualEncoder
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## Overview
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The [`VisionTextDualEncoderModel`] can be used to initialize a vision-text dual encoder model with
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any pretrained vision autoencoding model as the vision encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit)) and any pretrained text autoencoding model as the text encoder (*e.g.* [RoBERTa](roberta), [BERT](bert)). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
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to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
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downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
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training and then can be used for zero-shot vision tasks such image-classification or retrieval.
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In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how
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leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvement on
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new zero-shot vision tasks such as image classification or retrieval.
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## VisionTextDualEncoderConfig
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[[autodoc]] VisionTextDualEncoderConfig
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## VisionTextDualEncoderProcessor
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[[autodoc]] VisionTextDualEncoderProcessor
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<frameworkcontent>
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<pt>
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## VisionTextDualEncoderModel
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[[autodoc]] VisionTextDualEncoderModel
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- forward
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</pt>
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<tf>
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## FlaxVisionTextDualEncoderModel
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[[autodoc]] FlaxVisionTextDualEncoderModel
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- __call__
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</tf>
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<jax>
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## TFVisionTextDualEncoderModel
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[[autodoc]] TFVisionTextDualEncoderModel
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- call
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</jax>
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</frameworkcontent>
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