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 …
Y Zeng, X Zhang, H Li, J Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
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 …
J Hu, Y Yao, C Wang, S Wang, Y Pan, Q Chen… - arXiv preprint arXiv …, 2023 - arxiv.org
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 …