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[CVPR 2022] Sequential Voting with Relational Box Fields for Active Object Detection

The official code release for our paper

Sequential Voting with Relational Box Fields for Active Object Detection
Qichen Fu, Xingyu Liu, Kris M. Kitani
CVPR2022

[Paper][Project][Code]

Setup

  1. Clone this repository 
    git clone https://github.com/fuqichen1998/SequentialVotingDet
  2. Create a python environment and install the requirements 
    conda create --name seqvotingdet python=3.8
conda activate seqvotingdet
pip install -r requirements.txt

Experiments

Dataset Preparation

100DOH

Download and unzip the raw.zip and file.zip following the official 100DOH website, and put them under 100DOH_FOLDER. Modify the data folder in doh100.py accordingly.

MECCANO

Download and unzip the Active Objects Bounding Box Annotations and Frames and put it under MECCANO_FOLDER. Then download the pseudo labels in the Google Drive and put them under MECCANO_FOLDER/home/fragusa/. Modify the data folder in meccano.py accordingly.

Evaluation

Please download the pre-trained checkpoints of our model in the Google Drive, and put them under saved/models/. Then donwload the pre-computed annotation files in the Google Drive and put them under saved/.

100DOH

To evaluate on 100DOH, run the following command:

python test.py --resume saved/models/exp_doh100/checkpoint-epoch5.pth  -d 0 --ngpu 1 --use_gt_hand ""
python evaluate_100doh.py

MECCANO

To evaluate on MECCANO, run the following command:

python test.py --resume saved/models/exp_meccano/checkpoint-epoch5.pth -d 0 --ngpu 1
python evaluate_meccano.py

Train

Download the detected hand bounding boxes in the Google Drive and put it under saved/.

100DOH

To train on 100DOH, run the following command first to pretrain:

python train.py -c configs/doh100_dlv3+tr.json -d "0, 1, 2, 3"

Then run the following command to finetune the model using RL:

python train.py -c configs/doh100_dlv3+tr_rl.json -d "0, 1, 2, 3"

MECCANO

To train on MECCANO, run the following command first to pretrain:

python train.py -c configs/mcn_dlv3+tr.json -d "0, 1, 2, 3"

Then run the following command to finetune the model using RL:

python train.py -c configs/mcn_dlv3+tr_rl.json -d "0, 1, 2, 3"

Finally, please follow the Evaluation section to test the trained model.

Citation

Please consider citing our paper if it is helpful:

@inproceedings{fu2021sequential,
  title={Sequential Decision-Making for Active Object Detection from Hand},
  author={Fu, Qichen and Liu, Xingyu and Kitani, Kris M},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}

Acknowledgments