In this work, we present a conceptually simple and effective method to train a strong
bilingual/multilingual multimodal representation model. Starting from the pre-trained …

Unsupervised large-scale vision-language pre-training has shown promising advances on
various downstream tasks. Existing methods often model the cross-modal interaction either …

Image-text contrastive learning models such as CLIP have demonstrated strong task transfer
ability. The high generality and usability of these visual models is achieved via a web-scale …

Scaling up neural networks has led to remarkable performance across a wide range of
tasks. Moreover, performance often follows reliable scaling laws as a function of training set …

Abstract Vision-Language Pre-Training (VLP) has shown promising capabilities to align
image and text pairs, facilitating a broad variety of cross-modal learning tasks. However, we …

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 …

Machines that can represent and describe environmental soundscapes have practical
potential, eg, for audio tagging and captioning systems. Prevailing learning paradigms have …

We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-
form text-image composition and comprehension. This model goes beyond conventional …

Abstract Vision-Language Pretraining (VLP) has significantly improved the performance of
various vision-language tasks with the matching of images and texts. In this paper, we …

Most humans use visual imagination to understand and reason about language, but models
such as BERT reason about language using knowledge acquired during text-only …