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 …

Multimodal learning provides a path to fully utilize all types of information related to the
modeling target to provide the model with a global vision. Zero‐shot learning (ZSL) is a …

Large-scale multi-modal pre-training models such as CLIP and PaLI exhibit strong
generalization on various visual domains and tasks. However, existing image classification …

Zero-shot learning (ZSL) aims to predict unseen classes whose samples have never
appeared during training. One of the most effective and widely used semantic information for …

Deploying large language models (LLMs) to real scenarios for domain-specific question
answering (QA) is a key thrust for LLM applications, which poses numerous challenges …

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

Zero-shot learning (ZSL) enables the recognition of novel classes by leveraging semantic
knowledge transfer from known to unknown categories. This knowledge, typically …

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 …

The task of sequential model editing is to fix erroneous knowledge in Pre-trained Language
Models (PLMs) efficiently, precisely and continuously. Although existing methods can deal …

Visual grounding (VG) aims to locate a specific target in an image based on a given
language query. The discriminative information from context is important for distinguishing …