1 code implementation • CVPR 2023 • Dengsheng Chen, Jie Hu, Vince Junkai Tan, Xiaoming Wei, Enhua Wu
Federated learning enables the privacy-preserving training of neural network models using real-world data across distributed clients.
1 code implementation • 30 Sep 2022 • Dengsheng Chen, Jie Hu, Wenwen Qiang, Xiaoming Wei, Enhua Wu
In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain.
1 code implementation • 16 Sep 2021 • Dengsheng Chen, Vince Tan, Zhilin Lu, Jie Hu
Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation.
no code implementations • 5 Apr 2021 • Dengsheng Chen, Haowen Deng, Jun Li, Duo Li, Yao Duan, Kai Xu
In this work, rather than defining a continuous or discrete kernel, we directly embed convolutional kernels into the learnable potential fields, giving rise to potential convolution.
1 code implementation • 24 Jun 2020 • Dengsheng Chen, Jun Li, Kai Xu
Adding the attention module with a rectified linear unit (ReLU) results in an amplification of positive elements and a suppression of negative ones, both with learned, data-adaptive parameters.
no code implementations • CVPR 2020 • Dengsheng Chen, Jun Li, Zheng Wang, Kai Xu
To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category.
no code implementations • 12 Feb 2019 • Dengsheng Chen, Wenxi Liu, You Huang, Tong Tong, Yuanlong Yu
Detection and segmentation of the hippocampal structures in volumetric brain images is a challenging problem in the area of medical imaging.
no code implementations • 27 Sep 2018 • Wenxi Liu, Yibing Song, Dengsheng Chen, Shengfeng He, Yuanlong Yu, Tao Yan, Gerhard P. Hancke, Rynson W. H. Lau
In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance.