Recent pretrained vision-language models have achieved impressive performance on cross-
modal retrieval tasks in English. Their success, however, heavily depends on the availability …

In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of
processing either unimodal inputs (eg, image or language) or multimodal inputs (eg, the …

Multi-modal learning has become increasingly popular due to its ability to leverage
information from different data sources (eg, text and images) to improve the model …

Recently, multimodal transformer models have gained popularity because their performance
on downstream tasks suggests they learn rich visual-linguistic representations. Focusing on …

Multi-modal image-text models such as CLIP and LiT have demonstrated impressive
performance on image classification benchmarks and their zero-shot generalization ability is …

The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly
generating websites from handwritten text and identifying humorous elements within …

Large vision-language models, such as CLIP, learn robust representations of text and
images, facilitating advances in many downstream tasks, including zero-shot classification …

Abstract Contrastive Language-Image Pre-training (CLIP) has significantly boosted the
performance of various vision-language tasks by scaling up the dataset with image-text pairs …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

Contrastive language-image pretraining (CLIP) using image-text pairs has achieved
impressive results on image classification in both zero-shot and transfer learning settings …