Year: 2025
Type: article
Abstract: With the popularity of edge computation, object detection applications face challenges of limited data volume and data privacy. To address these, we propose a federated few-shot object detection framework, F2SOD. It involves three steps: collaborative base model training with base class data, novel data augmentation via an improved diffusion model, and collaborative base model fine-tuning for novel model using augmented data. Specifically, we present a data augmentation method based on diffusion models with a twofold-tag prompt construction and object location embedding. In addition, we present distributed framework for training base and novel models, where the base model integrates the Squeeze-and-Excitation attention mechanism into the feature re-weighting module. Experiments on public datasets demonstrate that F2SOD achieves efficient few-shot object detection, outperforming State-of-the-Art methods in both accuracy and efficiency.
Source: Electronics
Authors: Peng Li, Tianyu Zhang, Chen Qing, Shuzhuang Zhang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4409606426