Home

Awesome

PyTorch Implementation of ARN

Introduction

This repository is Pytorch implementation of Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding in ICCV 2019. Check our paper for more details.

Prerequisites

Installation

Please refer to MattNet to install mask-faster-rcnn, REFER and refer-parser2. Follow Step 1 & 2 in Training to prepare the data and features.

Training

Train ARN with ground-truth annotation:

CUDA_VISIBLE_DEVICES=${GPU_ID} python ./tools/train.py --dataset ${DATASET} --splitBy ${SPLITBY} --exp_id ${EXP_ID}

Evaluation

Evaluate ARN with ground-truth annotation:

CUDA_VISIBLE_DEVICES=${GPU_ID} python ./tools/eval.py --dataset ${DATASET} --splitBy ${SPLITBY} --split ${SPLIT} --id ${EXP_ID}

Citation

@inproceedings{lxj2019arn,
  title={Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding},
  author={Xuejing Liu, Liang Li, Shuhui Wang, Zheng-Jun Zha, Dechao Meng, and Qingming Huang},
  booktitle={ICCV},
  year={2019}
}

Acknowledgement

Thanks for the work of Licheng Yu. Our code is based on the implementation of MattNet.

Authorship

This project is maintained by Xuejing Liu.