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 …

Robotic vision applications often necessitate a wide range of visual perception tasks, such
as object detection, segmentation, and identification. While there have been substantial …

State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial
unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning using …

Recently, mixture of experts (MoE) has become a popular paradigm for achieving the trade-
off between modal capacity and efficiency of multi-modal large language models (MLLMs) …

The Mixture-of-Experts (MoE) has gained increasing attention in studying Large Vision-
Language Models (LVLMs). It uses a sparse model to replace the dense model, achieving …

The development of large language models (LLMs) has expanded to multi-modal systems
capable of processing text, images, and speech within a unified framework. Training these …

Vision Transformers (ViTs) have emerged as popular models in computer vision,
demonstrating state-of-the-art performance across various tasks. This success typically …

In recent years, multimodal large-scale pre-trained models have achieved tremendous
success and become a milestone in the field of artificial intelligence, demonstrating the …

Large language models (LLMs) have seen rapid advancements in their ability to integrate
and generate knowledge from various sources. However, the challenge of efficiently …

Integrated with multi-modal learning, knowledge graphs (KGs) as structured knowledge
repositories, can enhance AI for processing and understanding complex, real-world data …