“LiT: Zero-Shot Transfer With Locked-Image Text Tuning”, 2021-11-15 (; similar):
This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training.
In our empirical study, we find that locked pre-trained image models with unlocked text models work best. We call this instance of contrastive-tuning “Locked-image Text tuning” (LiT-tuning), which just teaches a text model to read out good representations from a pre-trained image model for new tasks.
A LiT-tuned model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT-tuning is widely applicable; it works reliably with multiple pre-training methods (supervised and unsupervised) and across diverse architectures (ResNet, Vision Transformers, and MLP-Mixer) using 3 different image-text datasets.
With the transformer-based pre-trained ViT-g/14 model, the LiT-tuned model achieves 84.5% zero-shot transfer accuracy on the ImageNet test set, and 81.1% on the challenging out-of-distribution ObjectNet test set.