most citedLarge-scale Continuous Gesture Recognition Using Convolutional Neural Networks

11 citations · 13 across the 2 of their papers we have counts for

collaborators

17 papers

cs.CL20251 cited

Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation

Guanting Dong, Xiaoxi Li, Yuyao Zhang +1

Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents.…

eess.IV2025

Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction

Haonan Zhang, Guoyan Lao, Yuyao Zhang +1

Quantitative magnetic resonance imaging (qMRI) provides tissue-specific parameters vital for clinical diagnosis. Although simultaneous multi-parametric qMRI (MP-qMRI) technologies…

eess.IV2025

Super-temporal-resolution Photoacoustic Imaging with Dynamic Reconstruction through Implicit Neural Representation in Sparse-view

Youshen Xiao, Yiling Shi, Ruixi Sun +3

Dynamic Photoacoustic Computed Tomography (PACT) is an important imaging technique for monitoring physiological processes, capable of providing high-contrast images of optical abso…

eess.IV20252 cited

SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction

Jiangjie Wu, Lixuan Chen, Zhenghao Li +6

High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D slices is crucial for clinical diagnosis. Reliable slice-to-volume registration (SVR)-based motion correctio…

cs.CR2025

: A Pluggable and Dynamic DoS-Defense Framework Against Resource Consumption Attacks Targeting Large Language Models

Yuanhe Zhang, Xinyue Wang, Haoran Gao +4

Large Language Models (LLMs), due to substantial computational requirements, are vulnerable to resource consumption attacks, which can severely degrade server performance or even c…

cs.CL20251 cited

Hierarchical Document Refinement for Long-context Retrieval-augmented Generation

Jiajie Jin, Xiaoxi Li, Guanting Dong +6

Real-world RAG applications often encounter long-context input scenarios, where redundant information and noise results in higher inference costs and reduced performance. To addres…