Pre-training a vison-language model and then fine-tuning it on downstream tasks have
become a popular paradigm. However, pre-trained vison-language models with the …

The exploration of multimodal language models integrates multiple data types, such as
images, text, language, audio, and other heterogeneity. While the latest large language …

Vision-Language (VL) models with the Two-Tower architecture have dominated visual-
language representation learning in recent years. Current VL models either use lightweight …

Abstract Large-scale Vision-and-Language (V+ L) pre-training for representation learning
has proven to be effective in boosting various downstream V+ L tasks. However, when it …

Recently, semantic segmentation models trained with image-level text supervision have
shown promising results in challenging open-world scenarios. However, these models still …

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, great success has been made in learning visual representations from text
supervision, facilitating the emergence of text-supervised semantic segmentation. However …

Pre-trained vision-language models (VLMs) have achieved impressive results in a range of
vision-language tasks. However, popular VLMs usually consist of hundreds of millions of …

Pre-trained multimodal foundation models have demonstrated remarkable generalizability
but pose challenges for deployment due to their large sizes. One effective approach to …

Vision and Language Pretraining has become the prevalent approach for tackling
multimodal downstream tasks. The current trend is to move towards ever larger models and …