122 citations · 123 across the 10 of their papers we have counts for
7 papers
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models
Weiying Zheng, Ziyue Lin, Pengxin Guo +3
Vision-Language Models (VLMs) have demonstrated remarkable capabilities in cross-modal understanding and generation by integrating visual and textual information. While instruction…
HSENet: Hybrid Spatial Encoding Network for 3D Medical Vision-Language Understanding
Yanzhao Shi, Xiaodan Zhang, Junzhong Ji +4
Automated 3D CT diagnosis empowers clinicians to make timely, evidence-based decisions by enhancing diagnostic accuracy and workflow efficiency. While multimodal large language mod…
A New Federated Learning Framework Against Gradient Inversion Attacks
Pengxin Guo, Shuang Zeng, Wenhao Chen +4
Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demon…
Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding
Zhiheng Cheng, Qingyue Wei, Hongru Zhu +4
The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However, its application in medic…
Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection
Weibo Jiang, Weihong Ren, Jiandong Tian +3
Human-Object Interaction (HOI) detection plays a vital role in scene understanding, which aims to predict the HOI triplet in the form of <human, object, action>. Existing methods m…
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning
Peiran Xu, Zeyu Wang, Jieru Mei +4
Federated learning (FL) is an emerging paradigm in machine learning, where a shared model is collaboratively learned using data from multiple devices to mitigate the risk of data l…