Overview
The [VisionTextDualEncoderModel] can be used to initialize a vision-text dual encoder model with
any pretrained vision autoencoding model as the vision encoder (e.g. ViT, BEiT, DeiT) and any pretrained text autoencoding model as the text encoder (e.g. RoBERTa, BERT). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
training and then can be used for zero-shot vision tasks such image-classification or retrieval.
In LiT: Zero-Shot Transfer with Locked-image Text Tuning it is shown how
leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on
