llmot / onestage_grounding

A Fast and Accurate One-Stage Approach to Visual Grounding, ICCV 2019 (Oral)

One-Stage Visual Grounding

***** New: Our recent work on One-stage VG is available at ReSC.*****

A Fast and Accurate One-Stage Approach to Visual Grounding

by Zhengyuan Yang, Boqing Gong, Liwei Wang, Wenbing Huang, Dong Yu, and Jiebo Luo

IEEE International Conference on Computer Vision (ICCV), 2019, Oral

Introduction

We propose a simple, fast, and accurate one-stage approach to visual grounding. For more details, please refer to our paper.

Citation

@inproceedings{yang2019fast,
  title={A Fast and Accurate One-Stage Approach to Visual Grounding},
  author={Yang, Zhengyuan and Gong, Boqing and Wang, Liwei and Huang
    , Wenbing and Yu, Dong and Luo, Jiebo},
  booktitle={ICCV},
  year={2019}
}

Prerequisites

• Python 3.5 (3.6 tested)
• Pytorch 0.4.1
• Others (Pytorch-Bert, OpenCV, Matplotlib, scipy, etc.)

Installation

1. Clone the repository

   git clone https://github.com/zyang-ur/onestage_grounding.git
   

2. Prepare the submodules and associated data

• RefCOCO & ReferItGame Dataset: place the data or the soft link of dataset folder under ./ln_data/. We follow dataset structure DMS. To accomplish this, the download_dataset.sh bash script from DMS can be used.

  bash ln_data/download_data.sh --path ./ln_data

• Flickr30K Entities Dataset: please download the images for the dataset on the website for the Flickr30K Entities Dataset and the original Flickr30k Dataset. Images should be placed under ./ln_data/Flickr30k/flickr30k_images.

• Data index: download the generated index files and place them as the ./data folder. Availble at [Gdrive], [One Drive].

  rm -r data
  tar xf data.tar
  

• Model weights: download the pretrained model of Yolov3 and place the file in ./saved_models.

  sh saved_models/yolov3_weights.sh
  

More pretrained models are availble in the performance table [Gdrive], [One Drive] and should also be placed in ./saved_models.

Training

3. Train the model, run the code under main folder. Using flag --lstm to access lstm encoder, Bert is used as the default. Using flag --light to access the light model.

   python train_yolo.py --data_root ./ln_data/ --dataset referit \
     --gpu gpu_id --batch_size 32 --resume saved_models/lstm_referit_model.pth.tar \
     --lr 1e-4 --nb_epoch 100 --lstm
   

4. Evaluate the model, run the code under main folder. Using flag --test to access test mode.

   python train_yolo.py --data_root ./ln_data/ --dataset referit \
     --gpu gpu_id --resume saved_models/lstm_referit_model.pth.tar \
     --lstm --test
   

5. Visulizations. Flag --save_plot will save visulizations.

Performance and Pre-trained Models

Please check the detailed experiment settings in our paper.

Dataset	Ours-LSTM	Performance ([email protected])	Ours-Bert	Performance ([email protected])
ReferItGame	Gdrive	58.76	Gdrive	59.30
Flickr30K Entities	One Drive	67.62	One Drive	68.69
RefCOCO		val: 73.66		val: 72.05
		testA: 75.78		testA: 74.81
		testB: 71.32		testB: 67.59

Credits

Part of the code or models are from DMS, MAttNet, Yolov3 and Pytorch-yolov3.