Search Results for author:
Found 21 papers, 3 papers with code
TSViT: A Time Series Vision Transformer for Fault Diagnosis
Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) face limitations in capturing temporal features (i. e., the variation of vibration signals over time).
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
Preliminary experiments show that enterprises can enhance and accumulate intelligent capabilities through multimodal model federated learning, thereby jointly creating an smart city model that provides high-quality intelligent services covering energy infrastructure safety, residential community security, and urban operation management.
FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning
Traditional federated learning uses the number of samples to calculate the weights of each client model and uses this fixed weight value to fusion the global model.
Predicting Token Impact Towards Efficient Vision Transformer
Token filtering to reduce irrelevant tokens prior to self-attention is a straightforward way to enable efficient vision Transformer.
SIEDOB: Semantic Image Editing by Disentangling Object and Background
Moreover, to produce high-quality edited images, we propose some innovative designs, including Semantic-Aware Self-Propagation Module, Boundary-Anchored Patch Discriminator, and Style-Diversity Object Generator, and integrate them into SIEDOB.
Reference-Guided Large-Scale Face Inpainting with Identity and Texture Control
To introduce strong control for face inpainting, we propose a novel reference-guided face inpainting method that fills the large-scale missing region with identity and texture control guided by a reference face image.
Context-Consistent Semantic Image Editing with Style-Preserved Modulation
Semantic image editing utilizes local semantic label maps to generate the desired content in the edited region.
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning
Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics.
TransPPG: Two-stream Transformer for Remote Heart Rate Estimate
Non-contact facial video-based heart rate estimation using remote photoplethysmography (rPPG) has shown great potential in many applications (e. g., remote health care) and achieved creditable results in constrained scenarios.
Feature-context driven Federated Meta-Learning for Rare Disease Prediction
Additionally, a dynamic-weight based fusion strategy is proposed to further improve the accuracy of federated learning, which dynamically selects clients based on the accuracy of each local model.
Federated Learning for Cross-block Oil-water Layer Identification
In this paper, we address this limitation by proposing a dynamic fusion-based federated learning(FL) for OWL identification.
Fed2: Feature-Aligned Federated Learning
Federated learning learns from scattered data by fusing collaborative models from local nodes.
Video Anomaly Detection By The Duality Of Normality-Granted Optical Flow
The normality-granted optical flow is predicted from a single frame, to keep the motion knowledge focused on normal patterns.
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4. 0 on the edge computing level.
A Survey of Hybrid Human-Artificial Intelligence for Social Computing
In particular with the boom of artificial intelligence (AI), social computing is significantly influenced by AI.
Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data
Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response.
Dynamic Fusion based Federated Learning for COVID-19 Detection
To improve communication efficiency and model performance, in this paper, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections.
Blockchain-based Federated Learning for Failure Detection in Industrial IoT
Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT.
Heterogeneous Federated Learning
Specifically, we design a feature-oriented regulation method ({$\Psi$-Net}) to ensure explicit feature information allocation in different neural network structures.
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning
In crowded scenes, detection and localization of abnormal behaviors is challenging in that high-density people make object segmentation and tracking extremely difficult.
Neural Aesthetic Image Reviewer
Through multi-task learning, the proposed models can rate aesthetic images as well as produce comments in an end-to-end manner.
