Learning visual representations from natural language supervision has recently shown great
promise in a number of pioneering works. In general, these language-augmented visual …

Spatial relations are a basic part of human cognition. However, they are expressed in
natural language in a variety of ways, and previous work has suggested that current vision …

Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

Vision language pre-training aims to learn alignments between vision and language from a
large amount of data. Most existing methods only learn image-text alignments. Some others …

Large-scale vision-language pretrained (VLP) models like CLIP have shown remarkable
performance on various downstream cross-modal tasks. However, they are usually biased …

Recently there has been a significant surge in multimodal learning in terms of both image-to-
text and text-to-image generation. However, the success is typically limited to English …

A modular design encourages neural models to disentangle and recombine different facets
of knowledge to generalise more systematically to new tasks. In this work, we assume that …

Recent advances in multimodal vision and language modeling have predominantly focused
on the English language, mostly due to the lack of multilingual multimodal datasets to steer …

Abstract Multilingual Vision-Language Pre-training (VLP) is a promising but challenging
topic due to the lack of large-scale multilingual image-text pairs. Existing works address the …

A modular design encourages neural models to disentangle and recombine different facets
of knowledge to generalise more systematically to new tasks. In this work, we assume that …