Search Results for author: Feifei Wang

Found 9 papers, 2 papers with code

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

Research and development investments are key to scientific and economic development 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.

Visual Prompting

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 +1

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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