Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Visual reasoning requires multimodal perception and commonsense cognition of the world.
Recently, multiple vision-language models (VLMs) have been proposed with excellent …

Abstract Knowledge-based Visual Question Answering (KB-VQA) requires VQA systems to
utilize knowledge from external knowledge bases to answer visually-grounded questions …

Multi-modal entity alignment (MMEA) aims to discover identical entities across different
knowledge graphs (KGs) whose entities are associated with relevant images. However …

As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to
identify identical entities across disparate knowledge graphs (KGs) by exploiting associated …

Knowledge-Intensive Visual Question Answering (KI-VQA) refers to answering a question
about an image whose answer does not lie in the image. This paper presents a new pipeline …

Large-scale vision-language pre-training has shown promising advances on various
downstream tasks and achieved significant performance in multi-modal understanding and …

Large-scale vision-language pre-training has achieved significant performance in multi-
modal understanding and generation tasks. However, existing methods often perform poorly …

In this work, we share our experience on tele-knowledge pre-training for fault analysis, a
crucial task in telecommunication applications that requires a wide range of knowledge …