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.

我们介绍了一种新的图像预训练模型 Llip，它通过模拟可能与图像匹配的多样的标题来提升图像的描述能力，并通过条件化输入信息来生成更丰富的视觉表示，相较于 CLIP 等基线模型，在多项任务上都有更好的性能表现，包括零样本分类和零样本检索。