Learning Vision from Models Rivals Learning Vision from Data
Yonglong Tian Lijie Fan Kaifeng Chen Dina Katabi Dilip Krishnan Phillip Isola
We introduce SynCLR, a novel approach for learning visual representations
exclusively from synthetic images and synthetic captions, without any real
data. We synthesize a large dataset of image captions using LLMs, then use an
off-the-shelf text-to-image model to generate multiple images corresponding to
each synthetic caption. We perform visual representation learning on these
synthetic images via contrastive learning, treating images sharing the same
caption as positive pairs. The resulting representations transfer well to many
downstream tasks, competing favorably with other general-purpose visual
representation learners such as CLIP and DINO v2 in image classification tasks.
Furthermore, in dense prediction tasks such as semantic segmentation, SynCLR
outperforms previous self-supervised methods by a significant margin, e.g.,
improving over MAE and iBOT by 6.2 and 4.3 mIoU on ADE20k for ViT-B/16.