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Found 10 papers, 6 papers with code
Scale-aware competition network for palmprint recognition
This paper proposes a scale-aware competitive network (SAC-Net), which includes the Inner-Scale Competition Module (ISCM) and the Across-Scale Competition Module (ASCM) to capture texture characteristics related to orientation and scale.
Energizing Federated Learning via Filter-Aware Attention
Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity.
SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data
This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023).
Privacy-Preserving Encrypted Low-Dose CT Denoising
In this way, the proposed methods can achieve two merits, the data privacy is well protected and the server model is free from the risk of model leakage.
Comprehensive Competition Mechanism in Palmprint Recognition
The traditional competition mechanism focuses solely on selecting the winner of different channels without considering the spatial information of the features.
Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification
Subsequently, we introduce anchor models for short- and long-spectrum, which constrain the optimization directions of local models associated with long- and short-spectrum images.
EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation
Recently, uncertainty-aware methods have attracted increasing attention in semi-supervised medical image segmentation.
(Re)$^2$H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning
Autonomous driving and its widespread adoption have long held tremendous promise.
Robust Split Federated Learning for U-shaped Medical Image Networks
To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL.
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging
The basic assumption of HyperFed is that the optimization problem for each institution can be divided into two parts: the local data adaption problem and the global CT imaging problem, which are implemented by an institution-specific hypernetwork and a global-sharing imaging network, respectively.
