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

• Install: pip install -r requirements.txt
• Prerequisites:
  • Taskonomy Dataset (tiny split)
  • BEiT pre-trained checkpoints (e.g., beit_base_patch16_224_pt22k)
  • Python environment
• Setup: Requires downloading datasets and pre-trained models, then configuring data_paths.yaml.
• Links: Taskonomy Dataset, BEiT

Highlighted Details

• Achieved Outstanding Paper Award at ICLR 2023.
• Supports multiple dense prediction tasks including semantic segmentation, depth estimation, and edge detection.
• Leverages pre-trained models like BEiT for strong feature extraction.

Maintenance & Community

• The project is associated with author Donggyun Kim.
• Code is official and released following the ICLR 2023 publication.

Licensing & Compatibility

• The repository does not explicitly state a license. However, it references and builds upon other projects, implying potential licensing considerations from those dependencies.

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.