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Learning Open-World Object Proposals without Learning to Classify

Pytorch implementation for "Learning Open-World Object Proposals without Learning to Classify" (RA-L and ICRA 2022)

Dahun Kim, Tsung-Yi Lin, Anelia Angelova, In So Kweon, and Weicheng Kuo.

@article{kim2021oln,
  title={Learning Open-World Object Proposals without Learning to Classify},
  author={Kim, Dahun and Lin, Tsung-Yi and Angelova, Anelia and Kweon, In So and Kuo, Weicheng},
  journal={IEEE Robotics and Automation Letters (RA-L)},
  year={2022}
}

Introduction

Humans can recognize novel objects in this image despite having never seen them before. “Is it possible to learn open-world (novel) object proposals?” In this paper we propose Object Localization Network (OLN) that learns localization cues instead of foreground vs background classification. Only trained on COCO, OLN is able to propose many novel objects (top) missed by Mask R-CNN (bottom) on an out-of-sample frame in an ego-centric video.

Cross-category generalization on COCO

We train OLN on COCO VOC categories, and test on non-VOC categories. Note our AR@k evaluation does not count those proposals on the 'seen' classes into the budget (k), to avoid evaluating recall on see-class objects.

Disclaimer

This repo is tested under Python 3.7, PyTorch 1.7.0, Cuda 11.0, and mmcv==1.2.5.

Installation

This repo is built based on mmdetection.

You can use following commands to create conda env with related dependencies.

conda create -n oln python=3.7 -y
conda activate oln
conda install pytorch=1.7.0 torchvision cudatoolkit=11.0 -c pytorch -y
pip install mmcv-full==1.2.7
pip install -r requirements.txt
pip install -v -e . 

Please also refer to get_started.md for more details of installation.

Prepare datasets

COCO dataset is available from official websites. It is recommended to download and extract the dataset somewhere outside the project directory and symlink the dataset root to $OLN/data as below.

object_localization_network
├── mmdet
├── tools
├── configs
├── data
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017

Testing

Our trained models are available for download here. Place it under trained_weights/latest.pth and run the following commands to test OLN on COCO dataset.

# Multi-GPU distributed testing
bash tools/dist_test_bbox.sh configs/oln_box/oln_box.py \
trained_weights/latest.pth ${NUM_GPUS}
# OR
python tools/test.py configs/oln_box/oln_box.py work_dirs/oln_box/latest.pth --eval bbox

Training

# Multi-GPU distributed training
bash tools/dist_train.sh configs/oln_box/oln_box.py ${NUM_GPUS}

Contact

If you have any questions regarding the repo, please contact Dahun Kim (mcahny01@gmail.com) or create an issue.