pinglmlcv / ARN

Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

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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

  • Python 3.5
  • Pytorch 0.4.1
  • CUDA 8.0

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.

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Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding


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