19 citations · 91 across the 13 of their papers we have counts for
13 papers
A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks
Jiaqi Wang, Hanqi Jiang, Yiheng Liu +21
In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (…
Brain Dialogue Interface (BDI): A User-Friendly fMRI Model for Interactive Brain Decoding
Heng Huang, Lin Zhao, Zihao Wu +5
Brain decoding techniques are essential for understanding the neurocognitive system. Although numerous methods have been introduced in this field, accurately aligning complex exter…
Understanding LLMs: A Comprehensive Overview from Training to Inference
Yiheng Liu, Hao He, Tianle Han +18
The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cos…
Holistic Evaluation of GPT-4V for Biomedical Imaging
Zhengliang Liu, Hanqi Jiang, Tianyang Zhong +47
In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial gener…
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Tianyang Zhong, Wei Zhao, Yutong Zhang +39
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and d…
Evaluating Large Language Models for Radiology Natural Language Processing
Zhengliang Liu, Tianyang Zhong, Yiwei Li +43
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they hav…
When Brain-inspired AI Meets AGI
Lin Zhao, Lu Zhang, Zihao Wu +11
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. T…
Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain
Xu Liu, Mengyue Zhou, Gaosheng Shi +6
Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the…
BI AVAN: Brain inspired Adversarial Visual Attention Network
Heng Huang, Lin Zhao, Xintao Hu +4
Visual attention is a fundamental mechanism in the human brain, and it inspires the design of attention mechanisms in deep neural networks. However, most of the visual attention st…
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations
Lin Zhao, Haixing Dai, Zihao Wu +8
Artificial neural networks (ANNs), originally inspired by biological neural networks (BNNs), have achieved remarkable successes in many tasks such as visual representation learning…
Rectify ViT Shortcut Learning by Visual Saliency
Chong Ma, Lin Zhao, Yuzhong Chen +7
Shortcut learning is common but harmful to deep learning models, leading to degenerated feature representations and consequently jeopardizing the model's generalizability and inter…
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention
Yiheng Liu, Enjie Ge, Mengshen He +6
Using deep learning models to recognize functional brain networks (FBNs) in functional magnetic resonance imaging (fMRI) has been attracting increasing interest recently. However,…
A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers
Yuzhong Chen, Yu Du, Zhenxiang Xiao +8
Vision transformer (ViT) and its variants have achieved remarkable successes in various visual tasks. The key characteristic of these ViT models is to adopt different aggregation s…