Search Results for author:
Found 6 papers, 4 papers with code
Merging Vision Transformers from Different Tasks and Domains
This work targets to merge various Vision Transformers (ViTs) trained on different tasks (i. e., datasets with different object categories) or domains (i. e., datasets with the same categories but different environments) into one unified model, yielding still good performance on each task or domain.
Foreign Object Debris Detection for Airport Pavement Images based on Self-supervised Localization and Vision Transformer
While a large and expensive dataset could be developed to include common FOD examples, it is infeasible to collect all possible FOD examples in the dataset representation because of the open-ended nature of FOD.
A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy
Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its wide applications in reality without violating the privacy regulations.
FOD-A: A Dataset for Foreign Object Debris in Airports
This paper presents the creation methodology, discusses the publicly available dataset extension process, and demonstrates the practicality of FOD-A with widely used machine learning models for object detection.
Integrative Use of Computer Vision and Unmanned Aircraft Technologies in Public Inspection: Foreign Object Debris Image Collection
Finally, several potential scenarios that could utilize either this dataset or similar methods for other public service are presented.
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists.
