This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

Abstract Knowledge-based visual question answering (VQA) requires external knowledge
beyond the image to answer the question. Early studies retrieve required knowledge from …

Multimodal learning, which aims to understand and analyze information from multiple
modalities, has achieved substantial progress in the supervised regime in recent years …

We present a novel methodology aimed at optimizing the application of frozen large
language models (LLMs) for resource-intensive vision-language (VL) pre-training. The …

Vision-language models, such as contrastive language-image pre-training (CLIP), have
demonstrated impressive results in natural image domains. However, these models often …

Abstract Pre-trained Visual-Language Models (VLMs), such as CLIP, have shown enhanced
performance across a range of tasks that involve the integration of visual and linguistic …

Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP, establish the
correlation between texts and images, achieving remarkable success on various …

Text-based person search (TBPS) aims to retrieve the images of the target person from a
large image gallery based on a given natural language description. Existing methods are …

Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP and ALIGN, have
introduced a new paradigm for learning transferable visual representations. Recently, there …

Advanced image fusion techniques aim to synthesise fusion results by integrating the
complementary information provided by the source inputs. However, the inherent differences …