Search Results for author:
Found 9 papers, 3 papers with code
FedGrad: Mitigating Backdoor Attacks in Federated Learning Through Local Ultimate Gradients Inspection
Federated learning (FL) enables multiple clients to train a model without compromising sensitive data.
High Accurate and Explainable Multi-Pill Detection Framework with Graph Neural Network-Assisted Multimodal Data Fusion
We then offer a framework for integrating a priori with pills' visual features to enhance detection accuracy.
CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization
By performing an in-depth analysis of the behavior of a classification model's penultimate layer, we introduce a metric that quantifies the similarity between two clients' data distributions without violating their privacy.
A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning
Medication mistaking is one of the risks that can result in unpredictable consequences for patients.
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning
The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning.
Image-based Contextual Pill Recognition with Medical Knowledge Graph Assistance
To this end, in this paper, we introduce a novel approach named PIKA that leverages external knowledge to enhance pill recognition accuracy.
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera
In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition.
Ranked #80 on
3D Human Pose Estimation
on Human3.6M
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data
We achieve promising results on real conditions of this dataset with the support of data augmentation and transfer learning techniques.
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks
For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs.
