PF-RPN: Prompt-Free Universal Region Proposal Network

🧠 Model Details

PF-RPN (Prompt-Free Universal Region Proposal Network) is a state-of-the-art model for Cross-Domain Open-Set Region Proposal Network, accepted at CVPR 2026.

Open-vocabulary detectors typically rely on text prompts (class names), which can be unavailable, noisy, or domain-sensitive during deployment. PF-RPN tackles this by revisiting region proposal generation under a strictly prompt-free setting. Instead of specific category names, all categories are unified into a single token (object).

Model Architecture Innovations

To improve proposal quality without explicit class prompts, PF-RPN introduces three key designs:

1. Sparse Image-Aware Adapter: Constructs pseudo-text representations from multi-level visual features.
2. Cascade Self-Prompt: Iteratively enhances visual-text alignments via masked pooling.
3. Centerness-Guided Query Selection: Selects top-k decoder queries using joint confidence scores.

Model Sources

• Repository: PF-RPN GitHub Repository
• Paper: PF-RPN: Prompt-Free Universal Region Proposal Network (CVPR 2026)
• Base Framework: MMDetection 3.3.0
• Backbone: Swin-Base (swinb)

🎯 Intended Use

• Primary Use Case: Generating high-quality, class-agnostic region proposals ("objects") across diverse, unseen domains without requiring domain-specific text prompts or retraining.
• Protocol: Strict one-class open-set setup where custom_classes = ('object',).

🗂️ Training Data

The provided checkpoint (pf_rpn_swinb_5p_coco_imagenet.pth) was trained on a combined dataset of COCO 2017 and ImageNet-1k.

• To simulate the open-set proposal generation task, all ground-truth categories are merged into a single class (object).
• The specific released model uses a 5% subset of the COCO training data merged with ImageNet images.

📊 Evaluation Data and Performance

PF-RPN achieves state-of-the-art Average Recall (AR) under prompt-free evaluation across multiple benchmarks.

Cross-Domain Few-Shot Object Detection (CD-FSOD)

Evaluated across 6 target domains (ArTaxOr, clipart1k, DIOR, FISH, NEUDET, UODD).

Method	Prompt Free	AR100	AR300	AR900	ARs	ARm	ARl
GDINO‡	✓	54.7	57.8	61.6	34.1	49.3	67.0
GenerateU	✓	47.7	54.1	55.7	28.1	48.3	69.4
Cascade RPN	✓	45.8	52.0	56.9	31.1	50.5	66.0
PF-RPN (Ours)	✓	60.7	65.3	68.2	38.5	61.9	80.3

Object Detection in the Wild (ODinW13)

Evaluated across 13 diverse target domains.

Method	Prompt Free	AR100	AR300	AR900	ARs	ARm	ARl
GDINO‡	✓	69.1	70.9	72.4	40.8	64.6	78.4
GenerateU	✓	67.3	71.5	72.2	32.8	63.1	80.0
Cascade RPN	✓	60.9	65.5	70.2	40.3	65.5	75.0
PF-RPN (Ours)	✓	76.5	78.6	79.8	45.4	71.9	85.8

(‡ indicates models where original class names were replaced with object to simulate a prompt-free setting).

⚙️ How to Use

Installation

Ensure you have a working environment with Python 3.10, PyTorch 2.1.0, and CUDA 11.8. Install MMDetection and this repository's codebase as described in the GitHub README.

Quick Start: Evaluation

1. Download the Weights

mkdir -p checkpoints

# Download GroundingDINO base weights
wget -O checkpoints/groundingdino_swinb_cogcoor_mmdet-55949c9c.pth \
  [https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swinb_cogcoor_mmdet-55949c9c.pth](https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swinb_cogcoor_mmdet-55949c9c.pth)

# Download PF-RPN weights
wget -O checkpoints/pf_rpn_swinb_5p_coco_imagenet.pth \
  [https://huggingface.co/tangqh/PF-RPN/resolve/main/pf_rpn_swinb_5p_coco_imagenet.pth](https://huggingface.co/tangqh/PF-RPN/resolve/main/pf_rpn_swinb_5p_coco_imagenet.pth)

2. **Run Inference / Testing**
```bash
python tools/test.py \
  configs/pf-rpn/pf-rpn_coco-imagenet.py \
  checkpoints/pf_rpn_swinb_5p_coco_imagenet.pth

Note: Data preprocessing is required before evaluation. Datasets must be downloaded and their categories merged into a single object class using the provided tools/merge_classes_and_sample_subset.py script. See the repository for detailed data preparation commands.

📚 Citation

If you use PF-RPN in your research, please cite:

@inproceedings{tang2026pf,
  title={Prompt-Free Universal Region Proposal Network},
  author={Tang, Qihong and Liu, Changhan and Zhang, Shaofeng and Li, Wenbin and Fan, Qi and Gao, Yang},
  booktitle={CVPR},
  year={2026}
}