8 citations · 9 across the 21 of their papers we have counts for
22 papers
A Language-Guided Multimodal Foundation Model for Zero-Shot and Multi-Task Brain Signal Analysis
Mingzhi Chen, Yiyu Gui, Guibo Luo +1
Brain signal analysis is essential for both neuroscience research and clinical diagnostics, yet current approaches face critical limitations. End-to-end models require task-specifi…
AgentFactory: Towards Automated Agentic System Design and Optimization
Enci Zhang, Haofeng Wang, Yuesheng Zhu +2
Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and complex task execution. Howev…
COMET: Contrastive Motion-Enhanced Temporal Reasoning for Video Multimodal Large Language Models
Chenghua Zhu, Zhaolu Kang, Qifan Shi +8
Video multimodal large language models have advanced significantly, yet fine-grained motion-temporal understanding remains fragile. The core bottleneck is not only sparse frame sam…
Attribute-Prompted Kernel Hashing for Unsupervised Data-Efficient Cross-Modal Retrieval
Runhao Li, Xiaoxu Ma, Zhenyu Weng +5
Unsupervised cross-modal hashing enables efficient retrieval of semantically related instances across different modalities without requiring manual semantic annotation. However, ex…
Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing
Runhao Li, Xiaoxu Ma, Zhenyu Weng +5
Compared to supervised cross-modal hashing (CMH), unsupervised CMH reduces the reliance on manual labeling by learning binary codes from unlabeled image-text pairs. However, existi…
SPOTR: Spatio-temporal Pooling One-Token Reconstruction for Universal Physiological Signal Self-supervised Learning
Yiyu Gui, Mingzhi Chen, Yuesheng Zhu +2
Physiological signals such as EEG, ECG, and PPG are widely used in clinical monitoring. Recent self-supervised learning (SSL) methods offer an attractive way to leverage unlabeled…
MedTS-TTT: Test-Time Training for Medical Time Series Classification
Mingzhi Chen, Yiyu Gui, Guibo Luo
Medical time series (MedTS) signals such as electroencephalography (EEG) and electrocardiography (ECG) support many clinical applications. However, substantial subject-level hetero…
DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models
Hanwen Zhang, Qiaojin Shen, Yuxi Liu +2
Foundation Models (FMs) have demonstrated strong generalization across diverse vision tasks. However, their deployment in federated settings is hindered by high computational deman…
Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences
Shudong Liu, Hanwen Zhang, Xiuling Wang +2
One-shot federated learning (OSFL) reduces the communication cost and privacy risks of iterative federated learning by constructing a global model with a single round of communicat…
Inference Attacks Against Graph Generative Diffusion Models
Xiuling Wang, Xin Huang, Guibo Luo +1
Graph generative diffusion models have recently emerged as a powerful paradigm for generating complex graph structures, effectively capturing intricate dependencies and relationshi…
A New One-Shot Federated Learning Framework for Medical Imaging Classification with Feature-Guided Rectified Flow and Knowledge Distillation
Yufei Ma, Hanwen Zhang, Qiya Yang +2
In multi-center scenarios, One-Shot Federated Learning (OSFL) has attracted increasing attention due to its low communication overhead, requiring only a single round of transmissio…
FedMP: Tackling Medical Feature Heterogeneity in Federated Learning from a Manifold Perspective
Zhekai Zhou, Shudong Liu, Zhaokun Zhou +4
Federated learning (FL) is a decentralized machine learning paradigm in which multiple clients collaboratively train a shared model without sharing their local private data. Howeve…
MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis
Jianhao Xie, Ziang Zhang, Zhenyu Weng +2
Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion models provide a potential solut…
LGM-Pose: A Lightweight Global Modeling Network for Real-time Human Pose Estimation
Biao Guo, Cong Zhou, Fangmin Guo +3
Most of the current top-down multi-person pose estimation lightweight methods are based on multi-branch parallel pure CNN network architecture, which often struggle to capture the…
VISTA: Mitigating Semantic Inertia in Video-LLMs via Training-Free Dynamic Chain-of-Thought Routing
Hongbo Jin, Jiayu Ding, Siyi Xie +2
Recent advancements in Large Language Models have successfully transitioned towards System 2 reasoning, yet applying these paradigms to video understanding remains challenging. Whi…
Federated Learning for Medical Image Classification: A Comprehensive Benchmark
Zhekai Zhou, Guibo Luo, Mingzhi Chen +2
The federated learning paradigm is wellsuited for the field of medical image analysis, as it can effectively cope with machine learning on isolated multicenter data while protectin…
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models
Mingzhi Chen, Yiyu Gui, Yuqi Su +3
Electroencephalogram (EEG) classification has been widely used in various medical and engineering applications, where it is important for understanding brain function, diagnosing d…
EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification
Yiyu Gui, MingZhi Chen, Yuqi Su +2
In recent years, with the development of deep learning, electroencephalogram (EEG) classification networks have achieved certain progress. Transformer-based models can perform well…
ProMamba: Prompt-Mamba for polyp segmentation
Jianhao Xie, Ruofan Liao, Ziang Zhang +3
Detecting polyps through colonoscopy is an important task in medical image segmentation, which provides significant assistance and reference value for clinical surgery. However, ac…
MPCPA: Multi-Center Privacy Computing with Predictions Aggregation based on Denoising Diffusion Probabilistic Model
Guibo Luo, Hanwen Zhang, Xiuling Wang +2
Privacy-preserving computing is crucial for multi-center machine learning in many applications such as healthcare and finance. In this paper a Multi-center Privacy Computing framew…
A Segmentation Foundation Model for Diverse-type Tumors
Jianhao Xie, Ziang Zhang, Guibo Luo +1
Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks. Many publicly available medical ima…
FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation
Yuxi Liu, Guibo Luo, Yuesheng Zhu
Medical image segmentation is crucial for clinical diagnosis. The Segmentation Anything Model (SAM) serves as a powerful foundation model for visual segmentation and can be adapted…