  There are a thousand ways to caption an image. Contrastive Language
Pretraining (CLIP) on the other hand, works by mapping an image and its caption
to a single vector -- limiting how well CLIP-like models can represent the
diverse ways to describe an image. In this work, we introduce Llip, Latent
Language Image Pretraining, which models the diversity of captions that could
match an image. Llip's vision encoder outputs a set of visual features that are
mixed into a final representation by conditioning on information derived from
the text. We show that Llip outperforms non-contextualized baselines like CLIP
and SigLIP on a variety of tasks even with large-scale encoders. Llip improves
zero-shot classification by an average of 2.9% zero-shot classification
benchmarks with a ViT-G/14 encoder. Specifically, Llip attains a zero-shot
top-1 accuracy of 83.5% on ImageNet outperforming a similarly sized CLIP by
1.4%. We also demonstrate improvement on zero-shot retrieval on MS-COCO by
6.0%. We provide a comprehensive analysis of the components introduced by the
method and demonstrate that Llip leads to richer visual representations.
