Search Results for author: Feifei Wang

Found 16 papers, 6 papers with code

Selective Aggregation for Low-Rank Adaptation in Federated Learning

1 code implementation2 Oct 2024 Pengxin Guo, Shuang Zeng, Yanran Wang, Huijie Fan, Feifei Wang, Liangqiong Qu

We investigate LoRA in federated learning through the lens of the asymmetry analysis of the learned $A$ and $B$ matrices.

Federated Learning General Knowledge +1

MambaClinix: Hierarchical Gated Convolution and Mamba-Based U-Net for Enhanced 3D Medical Image Segmentation

1 code implementation19 Sep 2024 Chenyuan Bian, Nan Xia, Xia Yang, Feifei Wang, Fengjiao Wang, Bin Wei, Qian Dong

Deep learning, particularly convolutional neural networks (CNNs) and Transformers, has significantly advanced 3D medical image segmentation.

Computational Efficiency Image Segmentation +4

Face Swap via Diffusion Model

1 code implementation2 Mar 2024 Feifei Wang

The basic framework consists of three components, i. e., IP-Adapter, ControlNet, and Stable Diffusion's inpainting pipeline, for face feature encoding, multi-conditional generation, and face inpainting respectively.

Face Alignment Face Detection +2

FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning

no code implementations CVPR 2024 Junyuan Zhang, Shuang Zeng, Miao Zhang, Runxi Wang, Feifei Wang, Yuyin Zhou, Paul Pu Liang, Liangqiong Qu

Federated learning (FL) is a powerful technology that enables collaborative training of machine learning models without sharing private data among clients.

Benchmarking Federated Learning

SimAC: A Simple Anti-Customization Method for Protecting Face Privacy against Text-to-Image Synthesis of Diffusion Models

1 code implementation CVPR 2024 Feifei Wang, Zhentao Tan, Tianyi Wei, Yue Wu, Qidong Huang

Despite the success of diffusion-based customization methods on visual content creation, increasing concerns have been raised about such techniques from both privacy and political perspectives.

Denoising Image Generation

Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data

no code implementations7 Dec 2023 Feifei Wang, Huiyun Tang, Yang Li

To address this issue, we develop a novel personalized federated learning framework for heterogeneous data, which we refer to as FedSplit.

Personalized Federated Learning

Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy

no code implementations27 Oct 2023 Hui Sun, Hao Luo, Feifei Wang, Qingjiu Chen, Meng Chen, Xiaoduo Wang, Haibo Yu, Guanglie Zhang, Lianqing Liu, JianPing Wang, Dapeng Wu, Wen Jung Li

Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit.

Defect Detection Image-to-Image Translation +1

Cooperation and interdependence in global science funding

no code implementations16 Aug 2023 Lili Miao, Vincent Larivière, Feifei Wang, Yong-Yeol Ahn, Cassidy R. Sugimoto

Investments in research and development are key to scientific and economic growth and to the well-being of society.

Diversity-Aware Meta Visual Prompting

1 code implementation CVPR 2023 Qidong Huang, Xiaoyi Dong, Dongdong Chen, Weiming Zhang, Feifei Wang, Gang Hua, Nenghai Yu

We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone.

Diversity Visual Prompting

Hierarchical Terrain Attention and Multi-Scale Rainfall Guidance For Flood Image Prediction

no code implementations4 Dec 2022 Feifei Wang, Yong Wang, Bing Li, Qidong Huang, Shaoqing Chen

With the deterioration of climate, the phenomenon of rain-induced flooding has become frequent.

Knowledge-Enhanced Relation Extraction Dataset

no code implementations19 Oct 2022 Yucong Lin, Hongming Xiao, Jiani Liu, Zichao Lin, Keming Lu, Feifei Wang, Wei Wei

Recently, knowledge-enhanced methods leveraging auxiliary knowledge graphs have emerged in relation extraction, surpassing traditional text-based approaches.

Entity Linking Knowledge Graphs +3

Jointly Dynamic Topic Model for Recognition of Lead-lag Relationship in Two Text Corpora

no code implementations21 Nov 2021 Yandi Zhu, Xiaoling Lu, Jingya Hong, Feifei Wang

To discover the lead-lag relationship, we propose a jointly dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus.

Dynamic Topic Modeling

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

1 code implementation CVPR 2022 Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin

Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.

Federated Learning

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