52 citations · 54 across the 8 of their papers we have counts for
10 papers
Information Bottleneck-Guided Adaptive Hypergraph Transformer for Brain Disease Diagnosis
Jingxi Feng, Xudong Chen, Yifan Zhang +5
Exploring high-order correlations and long-range dependencies in brain networks holds significant value for both neuroscience research and clinical diagnosis. However, previous stu…
Hyper-Fold: Exploring the Expressive Limit of Sequence-Geometry Learning for Proteins via Hypergraph Modeling
Yifan Feng, Guanjie Cheng, Shihui Ying +2
Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (three-dimensional geometry). W…
Count Anything
Mengqi Lei, Shuokun Cheng, Wei Bao +4
Object counting remains fragmented across domain-specific datasets and task formulations, despite rapid progress in generalist vision models. Existing counting models are often tai…
Hypergraph as Language
Mengqi Lei, Guohuan Xie, Shihui Ying +6
Large language models (LLMs) have recently shown strong potential in modeling relational structures. However, existing approaches remain fundamentally graph-centric: they focus on…
Hyper-KGGen: A Skill-Driven Knowledge Extractor for High-Quality Knowledge Hypergraph Generation
Rizhuo Huang, Yifan Feng, Rundong Xue +5
Knowledge hypergraphs surpass traditional binary knowledge graphs by encapsulating complex n-ary atomic facts, providing a more comprehensive paradigm for semantic representation.…
Cog-RAG: Cognitive-Inspired Dual-Hypergraph with Theme Alignment Retrieval-Augmented Generation
Hao Hu, Yifan Feng, Ruoxue Li +5
Retrieval-Augmented Generation (RAG) enhances the response quality and domain-specific performance of large language models (LLMs) by incorporating external knowledge to combat hal…
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
Mengqi Lei, Siqi Li, Yihong Wu +7
The YOLO series models reign supreme in real-time object detection due to their superior accuracy and computational efficiency. However, both the convolutional architectures of YOL…
SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition
Mengqi Lei, Yihong Wu, Siqi Li +4
Visual recognition relies on understanding the semantics of image tokens and their complex interactions. Mainstream self-attention methods, while effective at modeling global pair-…
Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation
Yifan Feng, Hao Hu, Xingliang Hou +5
Large language models (LLMs) have transformed various sectors, including education, finance, and medicine, by enhancing content generation and decision-making processes. However, t…
Hypergraph Foundation Model
Yue Gao, Yifan Feng, Shiquan Liu +4
Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks by connecting multiple vertices throu…