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 Tuning" (LiT), which just teaches a text model to read out good representations from a pre-trained image model for new tasks. A LiT model gains the capability of zero-shot transfer to new vision tasks, such as image classification or retrieval. The proposed LiT 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 three different image-text datasets. With the transformer-based pre-trained ViT-g/14 model, the LiT model achieves 85.2% zero-shot transfer accuracy on the ImageNet test set, and 82.5% on the challenging out-of-distribution ObjectNet test set.
@article{arxiv.2111.07991,
title = {LiT: Zero-Shot Transfer with Locked-image text Tuning},
author = {Xiaohua Zhai and Xiao Wang and Basil Mustafa and Andreas Steiner and Daniel Keysers and Alexander Kolesnikov and Lucas Beyer},
journal= {arXiv preprint arXiv:2111.07991},
year = {2022}
}
Comments
Xiaohua, Xiao, Basil, Andreas and Lucas contributed equally; CVPR 2022