Research paper for universal few-shot learning of dense prediction tasks
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This repository provides the official code for Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching, a method that achieved an Outstanding Paper Award at ICLR 2023. It enables efficient few-shot learning for various dense prediction tasks by leveraging a visual token matching approach, benefiting researchers and practitioners in computer vision.
How It Works
The core approach utilizes a "Visual Token Matching" strategy, which is a form of meta-learning. It trains a model to generalize across diverse dense prediction tasks with limited examples. The system likely employs a transformer-based architecture, pre-trained on a large dataset, and then meta-trained on a variety of tasks to learn transferable representations. This allows for rapid adaptation to new, unseen tasks with minimal data.
Quick Start & Requirements
pip install -r requirements.txt
beit_base_patch16_224_pt22k
)data_paths.yaml
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Maintenance & Community
Licensing & Compatibility
Limitations & Caveats
The setup requires significant data preparation and downloading of large pre-trained models. The specific license for the code itself is not clearly defined in the README, which may pose compatibility issues for commercial use.
1 year ago
Inactive