Distribution bias embedding tuning of vision transformer for remote sensing object detection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces Distribution Bias Embedding-based Tuning (DBET) to improve Vision Transformer (ViT) adaptation for remote sensing object detection. DBET enhances accuracy and efficiency by dynamically adjusting feature representations to bridge dataset distribution gaps.
Area Of Science
- Computer Vision
- Remote Sensing
- Machine Learning
Background
- Growing demand for advanced object detection in Earth observation due to increased remote sensing data availability.
- Vision Transformers (ViTs) show promise but face challenges adapting to remote sensing datasets due to distribution shifts.
- Fine-tuning ViTs for specific downstream tasks requires significant computational resources and time.
Purpose Of The Study
- To develop a novel method for adapting pre-trained Vision Transformers to remote sensing object detection tasks.
- To address the distribution bias between pre-training and downstream datasets.
- To improve computational efficiency and reduce training time for ViT fine-tuning.
Main Methods
- Introduction of Distribution Bias Embedding-based Tuning (DBET) method.
- DBET utilizes a bias embedding token to dynamically adjust feature representations.
- The method aims to align distributions and reduce the number of fine-tunable parameters while preserving the ViT structure.
Main Results
- DBET significantly improved accuracy on benchmark datasets: EAGLE (56.94%), RarePlanes (80.56%), and DOTA-v1.0 (70.68%).
- Reduced trainable parameters and accelerated fine-tuning process.
- Achieved an average training time reduction to 58% compared to full fine-tuning.
Conclusions
- DBET offers a scalable solution for remote sensing object detection by effectively adapting ViTs.
- The method enhances accuracy, particularly for datasets with limited categories.
- DBET improves computational efficiency, making ViT adaptation more practical for Earth observation applications.
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