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 …

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As
prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can …

The quadratic computation complexity of self-attention has been a persistent challenge
when applying Transformer models to vision tasks. Linear attention, on the other hand, offers …

The attention module is the key component in Transformers. While the global attention
mechanism offers high expressiveness, its excessive computational cost restricts its …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Self-attention mechanism has been a key factor in the recent progress of Vision Transformer
(ViT), which enables adaptive feature extraction from global contexts. However, existing self …

A transformer is the foundational architecture behind large language models designed to
handle sequential data by using mechanisms of self‐attention to weigh the importance of …

Image-text retrieval is a central problem for understanding the semantic relationship
between vision and language, and serves as the basis for various visual and language …

Abstract We introduce AutoVER, an Autoregressive model for Visual Entity Recognition. Our
model extends an autoregressive Multimodal Large Language Model by employing retrieval …

Pre-trained vision models (PVMs) have become a dominant component due to their
exceptional performance when fine-tuned for downstream tasks. However, the presence of …